Global AI Capital Moves at Full Speed – SwissCognitive AI Investment Radar

AI funding momentum hasn’t slowed. From global infrastructure projects to nuanced questions about investor confidence, this week brought high-dollar commitments alongside critical reflections on where the money is flowing—and why.

The United Arab Emirates made headlines with a bold $1.4 trillion, 10-year commitment to invest in the United States, a move that reflects the centrality of AI and tech collaboration in long-term statecraft. Meanwhile, BlackRock’s joint initiative with Microsoft, NVIDIA, and xAI signals continued investor appetite for large-scale AI infrastructure, with $100 billion earmarked for global data centers and energy solutions.

Several firms are also reinforcing their US presence: Hyundai announced a $21 billion investment, Siemens followed with $10 billion, and Schneider Electric added another $700 million—all aimed at fortifying AI-driven manufacturing and operations amid ongoing trade policy uncertainty.

Vietnam’s small businesses are setting the tone in Asia-Pacific, where 44% named AI their top tech investment for 2024. Fractal Analytics’ $13.7 million investment into India’s first reasoning model and Germany’s €2.1 million seed round for enterprise AI search show how national AI goals are increasingly shaped by local strategies and use cases.

Yet, not all attention is on infrastructure. Thought leaders at Man Group and other investment firms raised flags about the sustainability of AI stock valuations. An AI model under a top-performing fund has been flashing warnings on mega-cap tech stocks, including Nvidia. Still, audiences from pharma to finance are assessing AI’s value not just in terms of returns, but in ethics and relevance, particularly when it comes to pharma’s future and the realities of Artificial General Intelligence claims.

As global interest in AI capital remains high, this week’s updates highlight a shift from novelty to operational depth. More investment—yes—but also more scrutiny.

Previous SwissCognitive AI Radar: New AI Investment Funds and Strategic Expansions.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Global AI Capital Moves at Full Speed – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar

We can all agree that this week, the spotlight was firmly on DeepSeek, whose budget-friendly AI model sent shockwaves through the market, triggering the largest single-day market cap loss in history for Nvidia. Investors reacted sharply, fearing reduced demand for high-end semiconductor chips. While the immediate sell-off was staggering, some experts argue that DeepSeek’s innovation could expand AI adoption rather than collapse the market, potentially opening up new investment opportunities rather than diminishing them.

Beyond the DeepSeek turmoil, Microsoft continues its aggressive AI strategy, committing $80 billion to cloud expansion, leveraging OpenAI’s technology to solidify Azure’s competitive edge. Meanwhile, Meta’s $65 billion AI expansion aims to scale its infrastructure with massive data center investments, signaling confidence in AI’s long-term role in the tech industry.

Venture capital activity remains strong, with SoftBank eyeing a major investment in robotics startup Skild AI, valued at $4 billion. The startup aims to develop an AI-powered “brain” for more agile and dexterous robots, further integrating AI into automation and real-world applications. In the AI data space, Turing has tripled its revenue to $300 million, demonstrating the growing demand for AI training data as more companies scale up their AI models.

Looking beyond big tech, geopolitical AI strategies continue to unfold. India faces challenges in AI infrastructure, with investors warning that a lack of GPUs and data centers could hinder its global competitiveness. Meanwhile, the U.S. is contemplating a $500 billion AI infrastructure initiative, dubbed the Stargate Project, though experts question its feasibility given the sheer scale and energy demands.

As the AI market rapidly evolves, investors are looking for ways to maximize the value of their AI investments, from optimizing AI integration to structuring data and equipping teams with language models. Pharma investors are also weighing AI’s long-term potential, balancing high expectations with the reality of AI adoption hurdles in healthcare.

Despite the ups and downs of the market, AI investment remains a dominant force, shaping industries and redefining long-term strategies. Stay tuned for next week!

Previous SwissCognitive AI Radar: Who’s Investing and Why in AI.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: ucsf.edu – “4 Ways Artificial Intelligence (AI) is Poised to Transform Medicine”

AI can compare thousands of images to uncover dangerous patterns, create ultra-high resolution scans from low-res images and see what the human eye misses.

The radiologist was dead.

Or at least that’s what artificial intelligence (AI) experts prophesized in 2016 when they said AI would outperform radiologists within the decade.

Today, AI isn’t replacing imaging specialists, but its use is leading health care providers to reimagine the field. That’s why UC San Francisco was among the first U.S. universities to combine AI and machine learning with medical imaging in research and education by opening its Center for Intelligent Imaging.

Take a look at how UCSF researchers are pioneering human-centered AI solutions to some of medicine’s biggest challenges.

Spot illnesses earlier

Tens of thousands of Americans suffer pneumothoraces, a type of collapsed lung, annually. The condition is caused by trauma or lung disease – and serious cases can be deadly if diagnosed late or left untreated.

The problem:

This type of collapsed lung is difficult to identify: The illness can mimic others both in symptoms and in x-rays, in which only subtle clues may indicate its presence. Meanwhile, radiologists must interpret hundreds of images daily, and some hospitals do not have around-the-clock radiologists.

The solution:

UCSF researchers created the first AI bedside program to help flag potential cases to radiologists. In 2019, the tool was the first AI innovation of its kind to be licensed by the U.S. Food and Drug Administration. Today, it’s used in thousands of GE Healthcare machines around the world.

How did they do it?

Researchers from the Department of Radiology and Biomedical Imaging created a database of thousands of anonymous chest X-rays. Some of these images showed cases of collapsed lungs and others not.[…]

Read more: www.ucsf.edu

Der Beitrag 4 Ways Artificial Intelligence (AI) is Poised to Transform Medicine erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: eurekalert.org – “Artificial Intelligence-Based Chatbot Created for Bioimage Analysis”

Scientists from Universidad Carlos III de Madrid (UC3M), together with a research team from Ericsson and the KTH Royal Institute of Technology in Sweden, have developed an artificial intelligence-based software programme that can search for information and make recommendations for biomedical image analysis. This innovation streamlines the work of individuals using large bioimage databases, including life sciences researchers, workflow developers, and biotech and pharmaceutical companies.

The new assistant, called the BioImage.IO Chatbot and introduced in the journal Nature Methods, was developed as a response to the issue of information overload faced by some researchers. “We realised that many scientists have to process large volumes of technical documentation, which can become a tedious and overwhelming task,” explains Caterina Fuster Barceló, a researcher in the Department of Bioengineering at UC3M and one of the study’s authors. “Our goal was to facilitate access to data information while providing a simple interface that allows scientists to focus their time on bioimage analysis rather than programming,” she adds.

The chatbot can be a very useful tool, enabling researchers to perform complex image analysis tasks in a simple and intuitive manner. For example, if a researcher needs to process microscopy images using segmentation models, the chatbot can help select and execute the appropriate model.

The assistant is based on extensive language models and employs a technique called Retrieval-Augmented Generation (RAG), which enables real-time access to databases. “The main advantage is that we do not train the model with specific information; instead, we extract it from up-to-date sources, minimising errors known as ‘hallucinations’, which are common inaccuracies in other AI models like ChatGPT,” adds Arrate Muñoz Barrutia, professor in the Department of Bioengineering at UC3M and another author of the study.[…]

Read more: www.eurekalert.org

Der Beitrag Artificial Intelligence-Based Chatbot Created for Bioimage Analysis erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Advait Avinash Sowale – “Quality Dimensions of Generative AI Applications”

Generative AI the buzz word of today. Everyone is talking about it and using it for different purposes. The advantage of generative AI is not hidden today. Various sectors of society are using Gen AI in different areas like content creation, image designing, audio video creation and many others. People are authoring books using Gen AI.

Being an IT professional, we are usually never amazed with the results provided by Gen AI but look forward to making it better and better. Same as human, Gen AI tries to improve its last performance. We just help it to be there.

There are four distinct types of machine learnings. Supervised, Unsupervised, Semi-Supervised Learning and Reinforcement Learning. Till now we were surfing in the world of Supervised, Unsupervised, Semi-Supervised Learning by using these learning for the development of some smart applications, products and systems but the beauty of Gen AI is, it’s a first sphere towards the universe of Reinforcement Learning.

In many geographic the use of Gen AI is increased heavily. Various domains like Pharma, Education, Retail, Entertainment and many others are getting the solutions from Gen AI.

When we use traditional software, we do rigorous testing of the software. The software needs to pass extreme tests for functionality, performance, security, usability and for many other aspects.

Any AI enabled application minimizes human intervention. It is working on its own and hence it should work seamlessly. When we use any AI enabled application or systems, we minimize our dependency towards the task for which we have deployed the AI and therefore it is important to address the quality of that system.

For all the AI enabled applications rigorous testing is needed and Gen AI is not an exception to it.

Like in traditional testing, Gen AI testing includes Unit Testing, Integration Testing, System Testing, Functional Testing , Non-Functional Testing includes performance, usability and security.

The major difference between traditional and AI application testing with the dimensions of the quality parameters

The dimensions of the Gen AI testing majorly consist of Accuracy, Robustness, Ethics and Compliance. Extreme testing on these dimensions helps in making the Gen AI application a strong Gen AI application.

It is mandatory for the Gen AI to be a quality product because we are now transforming from the era of Weak AI to Strong AI.

When we talk about the quality of AI OR Gen AI application another aspect is the EAST.

So, what is EAST?

EAST stands for Explainability, Accountability, Security and Transparency.

These four aspects are utmost important when we are talking about comprehensive AI or specific to any AI like Gen AI.

The only context would be different. As Gen AI is providing the results for large numbers of types and pattern of data then it is mandatory to check for the explainability as how the outcome has been generated. With which process it is understanding the input, analyze the data and after processing, it is provided the output.

Accountability is another important aspect as there should be some responsible body, authority OR resource behind every output provided by Gen AI model. This can be achieved by maintaining and analyzing the entire process logs. Tracking of how the process is following defined ethical guidelines also helps to manage accountability.

Security has a vast spectrum for the Gen AI. Input, Content, analyzed data, output and learning and other miscellaneous all these factors are coming under security and for that it is needed to define security KPIs at various levels. Examples are Data, Authentication, Authorization, Incident Response, Vulnerability Management, User Behavior, Monitoring and many others. Data protection, model integrity and user privacy are some of the key factors which need to be addressed on the security front.

Finally, Transparency.

The output and the process of output should be transparent. Here the major important part is algorithm transparency. It leads to build the model confidence as transparency in algorithms is useful to understand how the algorithm works on different datasets. Model designing, Decision making process and overall process communication are among other factors which should consider to maintain the Transparency.

Now all these aspects are working to bring the quality of Gen AI model and add to that it is associated with the important factor of ethics. The Gen AI model should be ethically strong. Its output should not show any layer of bias and fairness. The points we have touched above must be considered at the extreme level with their KPIs and methodologies for the testing purpose.

Thus, the contribution of all these approaches and methodologies help to make a quality Gen AI.

About the Author:

Advait Avinash Sowale A Pune-based IT Professional with a decade of diverse expertise. Advait boasts an extensive career spanning over 14+ years, encompassing various domains such as Analysis, Designing, Development, Quality, and Delivery within the IT industry. Throughout his journey, he has contributed his skills and knowledge to renowned IT giants, catering to a global clientele.

Der Beitrag Quality Dimensions of Generative AI Applications erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

AI Innovations, Investments, and Market Leaders – SwissCognitive AI Investment Radar

Today’s SwissCognitive AI Investment Radar takes you across a spectrum of developments and strategic shifts shaping the field of AI investments.

Venture capital flows are increasingly directed towards AI startups, with significant contributions from industry giants like SoftBank and SK Networks in newly established funds. Meanwhile, corporations are exploring innovative financial strategies, such as activity-based costing, to optimize their AI investments and ensure they are reaping meaningful returns.

The rapid evolution of AI is also transforming industries beyond tech, with generative AI now driving over half of cloud investments worldwide, and companies like Lenovo reporting strong growth fueled by AI and services expansion. Yet, as businesses push forward, the challenges of measuring AI’s return on investment (ROI) persist, with some enterprises reconsidering their approaches.

From LG Electronics’ forward-looking investments in AI startups to Iceland’s PLAIO revolutionizing the pharma supply chain with AI, this week’s roundup offers a comprehensive view of how AI is reshaping industries, driving innovation, and influencing global markets.

Previous SwissCognitive AI Radar: AI Unicorns and Strategic Shifts.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag AI Innovations, Investments, and Market Leaders – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Ethan Millar – “Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail?”

Gaining deep insight with artificial intelligence tools is the trend for businesses to operate. Both small businesses and large enterprises are compelled to use AI technologies. AI chatbots communicate with more complex sessions. Companies that have already completed digital transformation should be moving towards a new generation of chatbots. SMEs can also take advantage of this new trend.

The new generation of AI chatbots comes with complex neural connections to have conversations. It is scalable as developers use deep learning tools. It eventually helps enterprises to bankroll AI-based intents with a high-tech approach. Unless the developer knows the pros, cons, and effects of deep learning tools on training chatbots, the very purpose of accurate deliverables gets lost in translation.

This 10-minute reading material is a virtual assistant for the developer to understand how deep learning tools maximize their potential. Moreover, it is also aimed at leadership with companies to understand why a bot-building project has met with failure. How can be brought back to work?

As both are inter-connected, this post focuses on IT developers in large enterprises and lean departments of small businesses.

Lessons to learn from the developer’s perspective

Have you just met with a failure in an AI Chatbot-based project, recently? It is not success that teaches. Failure adds a valued experience while dealing with different approaches to creating chatbots. Many companies fail initially in their efforts. It becomes the ideal base for understanding how a CRM developer can help an enterprise monetize through deep learning tools.

Three things count:

1. Deep learning does not involve or solve everything for business solutions. Some applications can do without it.
2. All enterprises cannot deal with specialized tools unless they have the requirement.
3. All developer tools are not meant for monetizing.

If an enterprise uses only deep learning tools, then only about 1/3^rd of its potential will be realized and the rest will remain untapped. The developer needs an overall understanding to tap it.

Two systems for learning

An IT team of a company) will need to research AI Chatbots and their specific requirements. It will avoid aberrations related to conversations with humans and machines. Earlier virtual assistants like Cortana, Siri, and Alexa set the bar for new bots. They still work with smartphones, appliances, and other home-based devices. They work on 2 systems – Supervised learning and unsupervised learning which require natural language processing capabilities. Since 2020, 85% of customers have been dealing with chatbots by making inquiries. The human connections have reduced.

Supervised Learning

The software is developed after getting data from real-world requests. Correlations are established between ‘tags’ and ‘user-intents’ which are marked for learning and engaging the customer. In such a case, deep learning tools achieve a high level of accuracy. Specialized tools are developed for this purpose. The only hitch here is if the data collected is insufficient or not suitable then the functionality and success are trapped.

Unsupervised learning

Again, in this case, too, a good database is required to understand the customer intent of the chatbot. When it is not supervised, it works independently. There is no need for human supervision while it functions nor does it require specific tags to prompt it to work.

The failure rate increases if the database does not provide a wide range of variables. The quality is not good enough for it to be released in the public domain. Even if it does come out, it will have limited success. The data volumes required are large for deep learning tools to be effective. And, it goes without saying that poor data does not give the required results and also affects business.

Chatbots will continue to grow

Despite the failure rate, AI chatbots will grow and many companies experiment with their capabilities. Consumers are already hooked on them and enjoy the services of such virtual assistants. They find an opportunity to add value to their routine tasks. Every public company wants to reduce customer care efforts, and this is a solution that has promise in the real world.

The only reason why it fails is due to the data required for tags and the user intent in each company is diverse. In some cases, it is limited to a certain extent. Hence, deep learning tools need careful deployment by the developer. They require a well-structured database and good examples for training the system. Getting advanced systems to work requires a good degree of inference latency, interpretability, and reproducibility to understand the data and train the program.

Developer’s skills are tested

A complex toolset may not be the answer for a training program to converse. It took years and several failed tests for Siri or Alexa to reach the stage where they are now. E-commerce giants using machine learning tools have survived as they have a constant flow of data to test and train. In the final analysis, a complete overview of components is required before they can be channeled and ready for public use or limited enterprise utility.

If developers choose hybrid systems, advanced NLP, and AI algorithms and do not rely on the 2 main systems there are bright chances of creating the right chatbot.

Now we turn our focus on the functionality and advantages of AI bots for real-time business needs.

AI chatbots are the new Jeeves

Your wish is my command!

Are you still confused about the diverse functions of AI deep learning? chatbots? Here is a simple description of the new automated ‘Jeeves’ in the corporate world. They are computer programs that communicate with the user as messengers. Some are advanced enough to handle instructions in the absence of the programmer.

It may sound like sci-fi but it is gaining traction as it is a time saver and do various tasks efficiently for different departments. For small businesses, it reduces overheads while multi-tasking.

How can it be deployed?

Most people are used to texting messages to each other as their main form of communication on social media or FB messenger even for work. This is the way even customer care is handled worldwide. Now chatbots are designed to take over.

Once you are familiar with deep learning and how it influences business processes the possibilities of its uses are unlimited. For example, they can be embedded in websites to answer 24×7 any customer queries. It is a live chat and once the user signs up on the website, the chatbot is functional.

Where is it most influential and popular? In businesses where customer services need to be handled with care. Today, pharma, real estate, and financial companies also use AI chatbots successfully.

Smart business advantage

Ai Chatbots are more common than you think. Google Assistant, Apple’s Siri and Amazon’s Alexa are all chatbots serving various functions. They are not only useful but are extremely popular. A smart chatbot increases your company’s visibility thereby boosting sales.

Earlier it was possible only for large companies to invest in AI deep learning. Now more avenues have opened up for small businesses to take advantage of this feature. Chatbots can be integrated into many areas of a company’s business. Chatbots use natural language processing in combination with machine learning to respond accurately to a customer’s requests.

They have been created to recognize an inquiry and provide an appropriate answer. With advances in the tool and features, they record previous questions and answers. They are geared to offer a personal experience to the user. As a service, it upgrades the company’s overall profile to settle disputes and provide customer satisfaction.

Ideal social media tools

AI chatbots have proven to be excellent social media marketing tools. Their efficiency is only set to increase in the coming years. AI provides personalized, real-time content targeting that produces 20 percent more sales opportunities. It can also be utilized for behavioral targeting methods for specific buyers. This is a sleek advantage for small companies that cannot hire expensive marketing managers.

Using this technology data and statistics prove to be useful to make decisions through predictive analysis. Machine learning can be applied in marketing to optimize for successful campaigns. Automaton reduces time gaps for performances and many sectors are turning towards bots to increase productivity and interactions.

Evolving innovation

With new developments, the way conversations are perceived is changing. This platform has already introduced voice bots and crypto tokens, messengers for blockchain.  Companies like Google, Apple, and Amazon are already developing new conversational platforms for better customer interaction. Perhaps this evolution will help solutions to be more forthcoming.

As 2024 is underway, the use of AI chatbots is no longer a luxury. It has become essential. With ChatGPT, Gemini, Bing, and Claude making an influential impact, it is hard to ignore them for business operations. Leaders require content generation and customization to streamline. AI bots can reason with limited inaccuracies with the user.

Closing thoughts

If you have failed once, now with experience take advantage of the new ‘Jeeves’ and its sophisticated commands. It’s time your developers take a fresh take on creating the right chatbot and reduce operational challenges.

About the Author:

Ethan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.

Der Beitrag Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Ethan Millar – “Why Should Healthcare Leaders Focus On Human-Machine Partnerships?”

Human-machine partnerships with AI and ML continue to trend and dominate clinical effectiveness. It supports new drug trials, APIs, and lab R&D increasing business revenue. It also has the potential to be sensitive towards patient care. The biggest transformation lies in a pivotal change: instead of ‘responsiveness’ the focus shifts to ‘prevention.’

AI technology facilitates the sensitive healthcare industry in diverse ways, primarily relying on emotional intelligence to succeed. For example, healthcare systems in developed nations find it an uphill task to keep the growing, aging population away from medical collapse. AI’s suitability and capability work wonders for patients’ physical and mental progress.

Healthcare leaders should make serious investments in human-machine partnerships to improve clinical effectiveness. Automation also supports revenue generation, organized operations, and handling of medical records. AI can benefit hospitals, specialty clinics, labs, and pharma companies.

Key drivers for successful AI dependencies

• How is AI evolving in healthcare outcomes?
• AI as a ‘human’ partner and powerful tool
• Clinical and admins benefit from automated features
• Support of virtual healthcare assistants
• Digital twin models
• Convergence of mental and physical well-being

How is AI evolving in healthcare outcomes?

 Humans have been using machines for a long time to save time and energy. Various tools have features with unique strengths and weaknesses that can be harnessed. In the healthcare industry, the human-machine partnership is being tested with the emergence of AI. The tools are being developed to augment better medical outcomes. They are already capable of transforming the operations of the medical fraternity. This disruption leads to more innovation for humans and machines to operate successfully.

How will man and machine collaboration become more effective in the coming years?

The secret lies in safety, accessibility, and affordability for patients at the core of this industry. The following table shows its effectiveness as AI evolves to deliver better patient care. Pharma and life sciences are likely to grow rapidly with AI technology.

AI as a human partner and powerful tool

AI tools add value and benefits primarily in two areas, keeping in mind patient care:

• Clinical section
• Administrative section

Big data includes a plethora of medical case studies involving genetics and innovation via technology. The most significant trends in the healthcare industry vis a vis big data include studies focused on:

1. Patient-oriented care– it begins with validation from researchers as they conduct trials in an organized way. Systematic data mitigates errors, increases quality control of services, reduces costs for patients from false readings or reports, and improves payment structures.
2. From drug development to commercialization– experts are outsourced for extensive trials in clinics, specialty hospitals and government facilities. Compliance with rules regarding time-to-market products is critical.
3. Pooling of unstructured data- it includes prescriptions, diagnostics, lab tests, patient care records, and insurance claims.

Clinical data management is bringing automation with:

• Complete data sharing between different medical experts to treat patients.
• Reliability of records during processing from one format to another.
• Fewer problems in large clinical trials are expected with the automation of data.

Multiple IDs can be created to make results more authentic during the crucial R & D process. The actual authorization is limited to a few personnel only. It ensures the trial design is secure and safe. Any clinical failure is rectified with the expertise of specific professionals involved during the audit. This drastically reduces false claims during trials with guidelines provided to meet future healthcare demands.

There are challenges in both areas while adopting AI capabilities. With future progress, the gaps can be bridged for the following:

• Data access is only for the authorized staff.
• Treatment disparity due to bias in patient trials. The disproportions are reduced.
• AI tools are difficult to control when evaluating results. Thus, transparency is compromised. New tools will create clarity.
• Privacy risks are never ruled out. As data is available to more organizations globally.
• The adoption of AI tools is restricted due to development by different parties. They are a liability or restriction for continued use.

There are other areas where innovative technologies are useful and complement human-machine partnerships.

How do virtual healthcare assistants help?

Due to the pandemic, many patients started seeking online help for medication, medical opinion, and continued care. Many clinicians are satisfied with virtual assistants to bond with patients. They can offer advice for treatment, diagnosis, and prescribing medicines. The assistants talk to patients and answer questions.

They offer answers and communicate with them. It reduces the need for patients to visit the doctor when they need urgent attention. AI chatbots can schedule and set up appointments. The new versions provide companionship to patients. It is useful as it keeps the patient mentally engaged and at peace!

Digital twins: modeling organs virtually

There is much hype regarding the emergence of ‘digital twins’ in the healthcare industry. Being unique, they provide digitized, personal, and custom options for the medical fraternity to work. This concept works on creating a ‘digital twin’ of a ‘person,’ ‘organ,’ or a ‘tool.’ It is ideal for testing drugs and wearable medical devices for patient usage. Thus, they pave the way for man and machine to deliver better results with the least effort.

In a hospital environment, they are created for lab results, studying computer models, and understanding the environment. While it is understandable that a hospital replica can be made for research and improvement, modeling human organs is challenging. With AI capabilities, it is possible. A treatment plan or a critical surgery can be conducted with the help of digital twins. In the future, more tools will be introduced to understand its benefits in real-time surgery with robots.

Convergence of mental and physical well being

While there are rapid advances in AI tools for physical health, the other side, mental health, cannot be ignored. It is more critical to take care of the patient’s mental health while offering treatments. It also assumes importance for the growing aged population in several developed nations. They need to survive with the least human interference.

New-age patients have different expectations from their healthcare service providers. Their needs are getting more complex. Converging man and machine to address this reality is a challenge. Being proactive can set the pace for delivering robust solutions with the combination of the emotional intelligence of doctors and the capabilities of artificial intelligence.

Is AI a wake-up call in this realm?

 Depression, anxiety, and loneliness form the deadly triumvirate that needs immediate attention. Globally, many adults suffer from mental illnesses while tackling long-term physical ailments. Depression is also a major contributor to diseases that take long to cure. AI tools like chatbots can detect depression risks in patients.

1. Early detection can provide better treatment options and get help from nurses, relatives, and volunteers.
2. AI bots can provide personalized recommendations, cures, or therapies. They collect feedback and prompts to offer advice.
3. Such AI tools are a boon in robust organizations and are effective for everyone’s well-being. Restrictions on privacy and access give confidence in people to interact with machines. This is yet another novel human-machine partnership that can be explored.
4. In organizations where diversity creates issues or there is sexual harassment, it can be mentally agonizing. AI can create a safe environment to express and get support.

With the help of AI Consulting solutions, the burden on healthcare systems will be reduced if organizations focus on the mental needs of their employees. If you are a part of the healthcare industry as a policy-maker or decisions taker the above content should be adopted with the best practices.

Conclusion

Where to start AI adoption in healthcare?

As a leader, where would you start your AI journey to ensure that the man and machine work successfully? Begin by opting for low-risk and high-impact solutions. Automation with data and smaller clinical trials is the best way forward. Move slowly and see the results before investing in this realm. Getting familiar with this technology will pave the way to use chatbots, virtual assistants, and digital twins more effectively.

Invest in clinical documentation and its time-consuming analysis. With AI’s predictive tool features the efforts are not wasted and present accurate results. Be a part of this revolution and meet the needs of patient care.

About the Author:

Ethan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.

Der Beitrag Why Should Healthcare Leaders Focus On Human-Machine Partnerships? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: techeconomy.ng – “The Evolution of Mental Health and the Emergence of AI: From Stigma to Greater Understanding”

However, in recent years, there has been a growing movement to break down the stigma surrounding mental health and promote greater understanding.

One of the most promising developments in this field is the emergence of artificial intelligence (AI). AI has the potential to revolutionize the way we diagnose, treat, and prevent mental health issues.

Tolulope Ijitade, Tech Entrepreneur and Advocate for Digital Transformation.

“In the fast-paced and pressurized world of innovation, real-time access to mental health support is no longer a luxury, but a necessity. Innovators push boundaries, challenge the status quo, and constantly face the unknown. This can take a toll on their emotional well-being. Real-time mental health support, powered by AI, can provide them with the tools and resources they need to navigate challenges, maintain resilience, and continue to thrive in their endeavors. Investing in accessible and immediate support for our innovators is not just about individual well-being, but about nurturing the very engine of our collective progress,” Tolulope Ijitade, Tech Entrepreneur and Advocate for Digital Transformation.

AI for Mental Health

AI-powered tools and applications are revolutionizing the way we approach mental health. These solutions offer a range of benefits, including:

• Increased accessibility: AI platforms can provide 24/7 access to resources and support, overcoming geographical limitations and reducing wait times for traditional therapy.
• Personalized care: AI algorithms can analyze individual data and tailor interventions to meet specific needs and preferences.
• Early detection: AI-powered tools can help identify early signs of mental health challenges, allowing for timely intervention and prevention of more severe issues.
• Empowerment: AI applications can empower individuals to take control of their mental well-being through self-management tools and personalized feedback.
• Stigma reduction: By normalizing conversations and promoting understanding, AI can play a crucial role in destigmatizing mental health issues.
• Cost-effectiveness: AI-powered solutions can also offer a more cost-effective alternative to traditional therapy, making mental healthcare more accessible for a wider population.

The integration of AI into mental health care presents a promising future for individuals seeking support. By leveraging the power of technology, we can break down barriers, foster greater understanding, and empower individuals to thrive.

As Nonye Ekpe, CEO of Balm.ai, aptly states: “AI has the potential to democratize access to mental health care and empower individuals to take charge of their well-being. We are witnessing a revolution in the field of mental health, and AI is at the forefront of this transformation.”

Nonye Ekpe, CEO of Balm.ai

Switzerland: A Powerhouse for AI in Health and Mental Health

Nestled amidst the Alps, Switzerland has emerged as a global leader in the field of AI innovation, particularly in the realm of health and mental health.

Renowned for its robust research ecosystem, strong academic institutions, and a supportive government, Switzerland fosters a unique environment where cutting-edge technologies like machine learning and deep learning are being harnessed to revolutionize healthcare delivery and mental health support.

From pioneering AI-powered tools that diagnose and personalize treatment plans for mental health conditions to developing AI-driven chatbots that offer accessible and stigma-free support, Switzerland is at the forefront of shaping a future where technology empowers mental well-being and empowers individuals to thrive.

Switzerland consistently stands out as a global powerhouse of innovation, education, and quality of life.

Internationally recognized for its unparalleled achievements, it holds leading positions in a variety of domains: being the most innovative country worldwide, boasting top-notch universities with unmatched international acclaim, and delivering an exceptional quality of life that resonates with both its citizens and expatriates.

These accomplishments, evidenced by numerous international rankings, emphasize Switzerland’s pivotal role in setting global benchmarks, driving businesses, industries, and societies toward excellence and sustainable growth.

Zurich, Switzerland stands out as a hub of AI innovation, marrying the country’s legacy of precision with technological advancement.

ETH Zurich, the Switzerland’s prominent institution, serves as a foundational pillar for AI research. Switzerland’s consistent emphasis on precision aligns perfectly with AI’s demand for accuracy. In addition, Zurich offers AI professionals a blend of technological growth, a stable economy, and an exceptional quality of life, ensuring both professional success and personal contentment.

Western Switzerland is a nexus for AI research and innovation, hosting world-renowned institutes like the EPFL in Lausanne, the Idiap Research Institute in Martigny and CAIM (Center for Artificial Intelligence in Medicine), which has been founded back in 2021 by the University of Bern and Bern University Hospital to shape the digital healthcare future.. It’s the birthplace of influential AI platforms like Torch.

Actors like the UN, ITU in Geneva and WEF in Davos are already managing important global AI activities from Switzerland. Building onto this reputation, Switzerland becomes the world leader for AI governance and ethically  implemented cognitive technologies.

The UN transferred their AI Center to Geneva back in 2017.  Also, the first AI for Good Global summit took place from 7 to 9 June 2017 in Geneva. Basel is the Health and Pharma center in Switzerland with Novartis and Roche.

A Case Study in AI-powered Healthcare Solutions from Switzerland

VAY.ai is a Swiss digital health startup based in Zürich, Switzerland that effortlessly digitizes human movements with a computer vision software.

Providing the highest personalization to users through precise motion analysis and real-time feedback for physical therapy, rehabilitation, and digital health apps, for accurate diagnosis, personalized treatment plans, and engaging therapy experiences, to achieve better health outcomes,.

This reduces stigma, increases accessibility, and empowers patients with tools for emotional regulation and coping skills, ultimately leading to a brighter future for mental well-being.

Biped.ai is an AI-powered navigation device maker for blind and visually impaired people. A Swiss Health Tech Solution.

Innovative Digital Healthcare Solutions in Nigeria

HealthConnect24×7 is a Nigerian digital healthcare startup based in Yaba, Lagos, Nigeria that combines next generation telemedicine, telemonitoring and home health to provide their customers with immediate access to highly trained and experienced doctors and wellness experts for acute and chronic condition management and advice via voice/video calls, live chat as well as on-site care.

Youper.ai, Effective mental health digital therapeutics powered by artificial intelligence.

Youper combines psychology and artificial intelligence to understand users’ emotional needs and engage in natural conversations.

A study published in the Journal of the American Medical Association identified Youper as the most engaging digital health solution for anxiety and depression.

Although, in the United States, innovative technology-based interventions have been developed to reduce stigma toward people with mental illness.

These interventions have demonstrated usefulness in stigma reduction and have summarized the latest advances, aligning with the growing interest and need for the application of new technologies in the field of mental health.

This evolving landscape reflects increasing mental health literacy and a reduction in stigmatizing attributions, marking a promising trend towards greater understanding and acceptance of mental health challenges.

Image credit: Screeshot of Youper.ai

AI Advancing Mental Health Solutions

With continued development and responsible implementation, AI has the potential to significantly improve the lives of millions of individuals around the globe.

The journey towards greater understanding and accessible mental health support for all is well underway, fueled by the power of AI and the dedication of individuals like Nonye Ekpe and her team at Balm.ai.

One way that AI is being used to improve mental health outcomes is by providing personalized treatment plans. AI algorithms can analyze data about a person’s mental health to identify the most effective treatment plan for them.

AI is also being used to improve the early detection of mental illness. AI algorithms can analyze data from wearable devices, such as smartphones and smartwatches, to identify early signs of mental illness.

The history of mental health care is laden with instances of cruelty and inhumanity inflicted upon those struggling with mental illness. From ancient societies that saw mental disturbances as divine punishment or demonic possession, through the dark ages of asylums and institutionalization, to the dawn of modern psychiatry, the journey of mental health care is a testament to societal evolution and our expanding understanding of the human mind.

Commencing with the digital revolution, the technologization of mental health care took on an unprecedented journey, from the internet’s widespread deployment to the expansive world of mobile applications and digital platforms.

The proliferation of smartphones brought mental health care to the palms of people’s hands, and the Internet’s accessibility facilitated the provision of therapy anywhere, anytime. More individuals began seeking help, and providers have reaped the rewards of technology’s reach.

Smartphone Users (Source: nairametrics.com)

And now, at the forefront of this journey lies artificial intelligence, a burgeoning field that, as if standing on the shoulders of history’s giants, promises to catapult mental health care into even greater heights.

AI has proven its potential across a variety of sectors such as healthcare, gaming, and finance, showcasing its ability to work creatively and symbiotically with human intelligence.

The tale of AI’s growing involvement in mental health care unfolds through several pioneering projects and use cases. From emotionally intelligent chatbots offering 24/7 mental health support to virtual reality exposure therapy, the range of AI-driven treatments and interventions is flourishing like never before.

AI’s promise lies in its ability to harness vast amounts of data through machine learning algorithms and extract insights that are unattainable by human capabilities alone.

For instance, AI’s proficiency in natural language processing allows it to analyze immense volumes of text-based data, helping psychologists in detecting and understanding patients’ emotional states with unparalleled precision.

This invaluable information aids providers in developing personalized treatment plans tailored to the unique needs of patients. Furthermore, AI’s potential in mental health accessibility is palpable, with the power to grant aid to those who have been out of reach for so long.

Significant developments in AI-driven mental health care solutions have been observed in various settings, from the workplace to personal self-care practices.

The integration of AI-powered emotional intelligence tools into organizational structures offers employees improved well-being and productivity, while AI-enhanced meditation applications lend newfound efficacy to the ancient art of mindfulness.

These noteworthy advancements in AI technology, while invaluable, must be tempered by prudent considerations of ethics, cross-cultural sensitivities, and potential limitations. The marriage of mental health care and AI can prosper only through a conscientious alliance that prioritizes empathy and personalized treatment.

As we stand at the precipice of a new era in mental health care, the future appears promising. Embracing AI-powered tools and technologies brings forth an unprecedented opportunity to revolutionize how we approach mental health, reach underserved communities, and build a more inclusive, healthier society.

It is upon the wings of AI that the field of mental health care can soar even higher, transcending boundaries, limitations, and leaving behind the shadowed past.

The advent of the internet as a platform for mental health care treatment marked a significant turning point. Online support groups, information dissemination, and teletherapy were now possible, democratizing mental health care and extending its reach to remote communities.

Those with limited access to care providers, or those preferring anonymity, benefited significantly from these novel technological advancements. It is essential to appreciate the inventive and meticulous minds who championed early technological advancements in mental health care.

Their efforts, emerging from a deep reservoir of human curiosity and fortitude, now serve as the bedrock upon which AI is poised to build a transformative, compassionate framework for the treatment and understanding of mental illness.

Our journey into understanding and harnessing the exceptional power of AI in mental health care is in its nascent stages – a testament to human ingenuity and determination. As we collectively embark upon this intrepid voyage, we heed the words of Vincent van Gogh: “Great things are done by a series of small things brought together.”

Each incremental advancement in AI technologies paves the way for future innovations, synergistically building upon the foundation established by preceding achievements.

In navigating the uncharted territory, we remain vigilant, steadfast, and humble – animated by the profound belief in a more compassionate, inclusive mental health care landscape. With AI at the helm of this brave new world, the possibilities that lie within our grasp are nothing short of extraordinary.

As we turn our gaze to the horizon, we cannot help but marvel at the possibilities that shimmer enticingly on the brink of realization, the fruition of pioneering visionaries’ daring pursuits.

In the face of daunting challenges, these early adopters dared to push the boundaries, transgress the status quo, and usher in what promises to be a brave new world—an intellectual awakening, coalescing at the nexus of artificial intelligence, neuroscience, and mental health.

In this theater of human connection, AI is poised to play a transformative role. Combining the seemingly incommensurable domains of human emotion and machine learning, AI-driven systems have the capacity to enhance our ability to connect with one another by refining our communication skills, elevating our emotional intelligence, and catalyzing shared empathy.

One can envision a world wherein AI-powered tools enhance our communications, sewing together the fragmented threads of our digital conversations and fostering greater emotional resonance between disparate or distanced individuals.

For example, imagine a couple struggling to maintain a long-distance relationship, grappling with disconnection and the challenges of communication in this era of separation.

Enter an AI-driven communication platform that not only facilitates their conversations, but also actively enriches their exchanges by detecting subtle emotional cues and offering suggestions for optimizing their emotional understanding and empathy.

By untangling the perplexing web of non-verbal cues that underpin our words, such a system might empower the couple to connect more deeply, rekindling their emotional bond despite the distance that separates them.

In this light, the potential of AI in enhancing social connection and communication for mental health becomes clear. By endowing these artificially intelligent systems with the ability to identify and interpret the complex emotional cues that infuse our language, touch our faces, and permeate the very spaces we inhabit, we arm ourselves with vital tools in the fight against loneliness and despair.

But the power of AI to shape our social connections extends beyond the realm of one-to-one communication. Consider, for instance, a community grappling with a collective sense of isolation, triggered perhaps by the strains of a global crisis.

Here, AI-driven systems that assess and map emotional states at a community level could offer deep insights into the shifting currents of this collective struggle.

By detecting emerging patterns of distress and identifying resources for support, such systems might serve as guiding beacons, illuminating the pathways towards connection and resilience for those mired in the darkness.

AI is a powerful tool that has the potential to revolutionize the way we diagnose, treat, and prevent mental illness. As AI technology continues to develop, we can expect to see even more innovative applications of AI in the field of mental health.

Regulation and Policy

As AI continues to develop, it is important to ensure that it is used in a responsible and ethical way. We need to make sure that AI is not used to perpetuate mental health stigma or to discriminate against people with mental illness. We also need to make sure that AI is used in a way that respects the privacy and autonomy of individuals.

With careful planning and implementation, AI has the potential to make a significant positive impact on the lives of people with mental illness. We can use AI to break down the stigma surrounding mental health, to improve access to mental health care, and to develop new and effective treatments for mental illness.

We believe that AI has the potential to make a real difference in the lives of people with mental illness. We are committed to working with others to ensure that AI is used in a way that promotes greater understanding and better mental health outcomes for all.

In this vision, the vast, untamed frontier of the human mind finds a worthy companion in the potent, agile hands of AI-driven applications, charting a course that is rife with unexplored possibilities and startling new discoveries. In the words of American philosopher, psychologist, and educational reformer John Dewey, “We do not learn from experience, we learn from reflecting on experience”.

As AI-guided mental health journeys continue to unfold, it is precisely this profound, reflective aspect of human consciousness that our cybernetic allies will help us nurture and enhance, leading us ever closer to the goal of global happiness and emotional resilience.

Original article: The Evolution of Mental Health and the Emergence of AI: From Stigma to Greater Understanding

Der Beitrag The Evolution of Mental Health and the Emergence of AI: From Stigma to Greater Understanding erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Nancy Kapila, Team Lead, Roots Analysis – “AI in Clinical Trials: How Artificial Intelligence is Shaping Clinical Trials”

The current wave of advanced biologics, vaccines, and therapeutic drugs is shaping the pharmaceutical industry. Clinical trials play a crucial role in the success of novel therapeutic development, consuming almost 50% of the time and capital investment during R&D processes. It is a well-known fact that the success rate of clinical trials is only 10-20% due to inappropriate study design, insufficient patient recruitment, false predictions, and improper protocol adherence. Above all these factors, accurate data predictions play a major role in the success of drug development. To address these concerns, Artificial Intelligence emerges as a most disruptive technology, enabling automation, advanced analytics, and real-time data prediction—saving plenty of time wasted on data collection and patient recruitment for drug efficacy and safety analysis. Ironically, when combined with advanced technology such as digital twins or organ-on-a-chip, AI in clinical trials streamlines clinical trial data and reduces human errors in data collection.

Importance of AI in Clinical Trial

The conventional drug development process is extremely complex and time-consuming. On average, it takes around 10 years to develop a novel drug and a capital investment of around USD 2.5 billion. Clinical trial failure delays the supply of drugs into the market and puts a financial burden on pharma companies. During clinical trials an enormous amount of data has been generated and requires detailed evaluation. Analysis and management of large amounts of data can be challenging without robust analysis tools.  However, with AI-driven analysis software and technologies data analysis becomes more streamlined and eliminates the chances of errors. Artificial intelligence is a sub-field of computer science, and datasets encompass machine learning and deep learning to analyze and interpret data.

AI in clinical trials plays a massive role, automating multiple tasks that consume most of the time during R&D processes. With the integration of artificial intelligence, researchers can easily track things such as drug identification, patient selection, data analysis, identification of drug patterns, and adverse impact of drugs. Therefore, AI in clinical trials shortens the time required for data analysis and significantly improves the success rate with real-time prediction. AI analytical tools can be used as prediction models to accelerate clinical studies such as disease identification and suitable patients and support clinical study designs. According to Roots Analysis the global AI in clinical trials market size  is estimated to be worth $ 8.5 billion in 2035, growing at a CAGR of 16% during the forecast period 2023-2035

Regulatory Status on Using AI in Clinical Trials

The U.S. Food and Drug Administration is highly committed to data to ensure drugs are effective and safe for human consumption. AI in clinical trials undoubtedly plays an essential role in fetching accurate data. Based on this, the FDA facilitates innovation in drug development and adopts a flexible risk-based regulatory framework that enhances technology innovation while safeguarding patient health. As a crucial part of bringing flexibility, In May 2023, the FDA’s Center for Drug Evaluation and Research has issued an initial discussion paper in collaboration with the Center for Biologics Evaluation and Research and the Center for Devices and Radiological Health. This paper has been published to address necessary considerations for using AI in clinical trials and drug development such as data quality, human-led government and model development standards. The regulatory authorities continue asking for feedback on the importance of advancing regulatory science in this field.

Applications of AI in Clinical Trials

AI in clinical trials helps to uncover valuable data insight during drug development that remains hidden. A clinical trial involves data analysis, patient recruitment, documentation and other data validation procedures that are crucial to get approval from the FDA.  AI-enabled technologies are therefore becoming a crucial part of the critical trials in the following areas.

1. AI in Clinical Trial Design

The adoption of AI in clinical trials by biopharma companies brings innovation to trial designs, effortlessly increasing the analysis of drug discovery data collected during clinical trials. AI-driven analytical tools help in quick comparison between current and past results of clinical trials. Integrating AI-enabled technologies also supports patient programs, post-market surveillance and has unparalleled potential to analyze, organize, or collect data generated during clinical trials. In simple terms, clinical trial design becomes more simplified and enables extraction of meaningful information such as drug failure, and adverse impact of drugs.

2. Site Selections

The most crucial aspect of clinical trials is the selection of highly functional investigator sites. Many parameters are taken into consideration when deciding investigator sites, such as administrative procedures, experienced clinicians, disease understanding, and resource availability. These qualities must be considered while selecting the site, as they influence data quality, integrity, and study timelines. AI in clinical trials helps pharmaceutical companies identify targeted sites and qualified investigators and collect evidence to meet regulatory standards. This ensures the clinical trial process adheres to Good Clinical Practice requirements.

3. Patient Enrichment, Enrolment and Recruitment

AI-driven transformation helps to improve patient selection and enhance clinical trial effectiveness through the proper analysis, mining, and interpretation of data through multiple resources. When combined with big data analytical tools, artificial intelligence helps interpret sources such as electronic health records and medical imaging data, thereby helping in patient recruitment.

4. Patient Medication Adherence, Monitoring and Retention

AI in clinical trials helps in patient medication monitoring and management by automating the capturing process. Combining AI algorithms with wearable digital technologies enables real-time insights and continuous patient monitoring. Moreover, AI algorithms also help fetch data about treatment effectiveness and safety while analyzing the risk of dropouts, enhancing patient engagement and retention.

5. Accurate use of Operational Data to Enhance AI-based Clinical Trial Analytics

Clinical trials generate a high amount of operational data. However, functional data disparate systems silos may hinder companies from receiving a comprehensive view of clinical trials portfolio across different global sites. Gathering information from any source into a common analytics platform backed with open data standards may promote integration while offering insights into important indicators. When combined with data visualization tools, a self-learning system intended to make better predictions and recommendations over time can proactively provide users with trustworthy analytics insights.

Limitations of AI in Clinical Trials

Though AI in clinical trials possesses enormous benefits, several limitations still have to be addressed. One of the potential limitations is the lack of standardization and data quality. Artificial intelligence relies on quality data to accurately predict and identify clinical trial patterns. Hence, the AI algorithm may not provide accurate prediction if the data is biased, inconsistent, or incomplete.

• Data security and privacy are major concerns when using AI in clinical trials. Patient data is crucial yet sensitive; it has been protected from being accessed by unauthorized parties. Clinicians must take appropriate steps to ensure complete privacy and security of patient data.
• Regulatory considerations have to be followed before implementation of AI in clinical trials. Though regulatory authorities such as the FDA have given flexibility in using AI for data analysis, using AI in clinical trials still raises concerns about data accountability and decision-making.

Paving Into the Future Vision of AI in Clinical TrialsTrails

Despite limitations, AI in the clinical trials market continually evolves to adopt and implement advanced technologies to improve data prediction while adhering to safety standards. The ongoing innovations show that AI in clinical trials holds a promising future.

Digital Twins Facilitate Real-Time Prediction

Digital twins, when combined with AI algorithms, generate virtual images that resemble patients’ physiological characters. This helps to achieve real-time insight into drug effects and individual health and enables the study of adverse drug impact. Currently, several pharmaceutical companies have begun to adopt digital twin technology monitored by AI to predict biological responses based on biomarkers.

AI Helps With Documentation

In the future, AI algorithms may expedite the regulatory documentation process and speed up the introduction of new drugs into the market.

Protocol Generation

AI language programs help create the first draft of clinical trial protocols using data inputs from previous trials, published literature, and multiple medical resources.

AI In Clinical Trials: Future of R&D Processes

Pharmaceutical companies are all set to pave into the storm of AI to develop tailored therapies that help treat diseases. As more novel drugs and therapies enter clinical studies, implementing AI in clinical trials becomes more feasible and important to shorten the time for drug development. Furthermore, flexibility in the regulatory guidelines for using artificial intelligence and machine learning in drug development encourages biopharma companies to accept innovation in clinical trials and implement cybersecurity to prevent data breaching issues.

In the future, AI / ML may combine with computer simulations and use advanced computer modeling in the regulatory evaluation of therapeutic drugs. Therefore, increasing implementation of virtual trials leveraging innovative digital technologies and artificial intelligence to lessen the financial burden and time required for drug development—bridging the gap between drug discovery and market supply.

Although AI hasn’t been included in clinical trials too often, it has the potential to revolutionize the process of developing new treatments. AI applications might make Clinical trials faster, safer, and far less expensive. The aim of biopharma to more thoroughly integrate patient-centricity across the whole R&D process will be achieved in part by the potential of AI to enhance the patient experience.

References

1. https://www.nature.com/articles/s41746-019-0148-3
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974218/
3. https://www.forbes.com/sites/greglicholai/2023/10/04/
4. https://www.rootsanalysis.com/reports/ai-based-drug-discovery-market.html

About the Author:

Nancy Kapila is a seasoned pharmaceutical consultant with over 5 years of varied experience and a Master’s in Pharmaceutics from Panjab University. She excels in drug mechanisms and interactions. Her career highlights include collaborating with numerous pharmaceutical companies and offering strategic insights and guidance. Nancy stands out for her dedication to keeping abreast of pharmaceutical advancements, regulatory changes, and emerging trends. She believes in continuous learning to navigate the industry’s complexities and provide innovative client solutions. Fascinated by the role of data analytics in decision-making, Nancy delves into data to uncover patterns and opportunities, offering evidence-based recommendations for process optimization, product development, and operational efficiency. Her career is driven by a relentless pursuit of knowledge, passion for data insights, and commitment to leading pharmaceutical companies towards success in a dynamic industry.

Der Beitrag How Artificial Intelligence (AI) is Shaping Clinical Trials erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.