Artificial Intelligence (AI) in Life Sciences and Healthcare

posted in Life Sciences

AI appeared to burst onto the scene in early 2023 and seems to be everywhere, all at once. In reality, AI technology has been integrated into our daily lives for a number of years, most visibly as experienced through the virtual assistants, Siri and Alexa, and its use in medical diagnostic software systems.

According to Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research, “Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work.”

The story of AI began many years ago, with researchers exploring the idea that machines could mimic human intelligence.

What Is AI?

What exactly is AI? AI makes it possible for machines to learn from experience, recognize patterns, instantly adjust based on new information, and perform human-like tasks, such as learning, reasoning, self-correction, and prediction based on data. Machines can do so at speeds far beyond the human brain's capacity. 

AI applications within the life sciences arena emerged in the 1970s as tools to analyze datasets and determine patterns. These machine learning algorithms proved able to sort through enormous troves of clinical data, accurately capture trends, and even anticipate certain outcomes. It was not just AI's capability to harness and analyze data that astounded researchers, but the fact that the algorithms could do so in a tiny fraction of the time it would take a human.

As the field of genomics began to emerge, scientists realized the vast amounts of data they could access needed computational processes: the human brain did not have the time or capacity to harness the data and pick up on themes hidden within. AI became a crucial solution in cracking the human genome in its ability to anticipate protein structures, analyze genetic sequence, and recognize mutations and anomalies. AI's superior ability to crack gene sequencing more economically and with greater efficiency has allowed medical researchers to expand their knowledge of disease and opened the door to the concept of personalized medicine.

AI and Medical Imaging

In recent years, the integration of machine learning into various fields of medicine has brought about remarkable advancements. One fascinating application is in the realm of medical imaging. AI and Machine Learning have demonstrated an extraordinary ability to analyze complex medical images, such as MRIs, with a level of precision surpassing the human brain's capabilities. 

Imagine a radiologist examining an MRI scan for signs of a potential tumor. While human expertise is invaluable, it is not infallible. Fatigue, distractions, or sheer complexity can lead to oversight or misinterpretation. However, with machine learning algorithms, AI can analyze every pixel of an image, identifying even the subtlest irregularities that might go unnoticed by the human eye. This newfound accuracy enhances diagnostic confidence and paves the way for early detection and intervention, potentially saving lives.

AI and Drug Discovery

Beyond crunching numbers and processing vast datasets, AI has become an indispensable tool in revolutionizing the drug development process. Traditionally, the process of identifying a novel drug candidate has involved years of painstaking research, costly experiments, and countless trials and errors. AI has flipped this script entirely.

With AI, researchers can now rapidly sift through immense datasets to identify promising drug compounds. AI's ability to predict a drug's potential efficacy and foresee possible adverse events in the preclinical research phase is revolutionary.

Another drug discovery challenge mitigated by AI is its capacity to review and interpret medical journal text. Again, where previously this was a manual process for humans, AI large language models can perform these tasks in a fraction of the time, harnessing the information found in massive volumes of medical text from across the world and pinpointing any trends that might be hidden there.

AI-driven robots are making the complex process of drug discovery and production more efficient and cost-effective. These robots can automate a range of laboratory tasks. They can perform some of the basic tasks of a chemist, such as handling chemicals and conducting small experiments, with enormous precision. Research can be faster and more efficient, and the risk of exposure to chemicals by human lab staff is reduced.

AI and Personalized Medicine

AI's proficiency in analyzing an individual's genetic makeup is groundbreaking on its own. When AI integrates that information with comprehensive assessments of lifestyle and medical records, including prior disease, drug allergies, and family background, healthcare recommendations can be tailored precisely to each patient's genetic predispositions, lifestyle choices, and medical history. 

Ethical Concerns and Limitations

The integration of AI in medicine, healthcare, and life sciences offers many benefits. It also raises complex ethical questions and highlights the limitations of machine learning. As stated succinctly by the FDA in a recent discussion paper

The use of AI/ML has the potential to accelerate the drug development process and make clinical trials safer and more efficient. However, it is essential to assess whether the use of AI/ML introduces specific risks and harms. For example, AI/ML algorithms have the potential to amplify errors and preexisting biases present in underlying data sources and, when the findings are extrapolated outside of the testing environment, raise concerns related to generalizability and ethical considerations. Additionally, an AI/ML system may exhibit limited explainability due to its underlying complexity or may not be fully transparent for proprietary reasons. These concerns have resulted in a focus on developing standards for trustworthy AI that address specific characteristics in areas such as explainability, reliability, privacy, safety, security, and bias mitigation.

One significant concern referenced above is the issue of data privacy and security. AI systems often require access to extensive patient data, including medical records, genetic information, and even lifestyle details. Another danger highlighted is the potential for algorithms to amplify errors and preexisting biases present in underlying sources.  Ensuring that AI algorithms are developed and deployed to safeguard patient confidentiality and mitigate bias is essential to maintaining trust in healthcare systems.

This is readily apparent in connection with gene editing technologies like CRISPR-Cas9. These technologies have the potential to revolutionize the field of genetics and medicine, but they also raise significant ethical and bias concerns that require careful consideration, such as the possibility of unintended health consequences or gene modifications that may affect future generations. Developing comprehensive ethical frameworks and well thought out regulations will be crucial to ensure that gene editing is used responsibly while minimizing harm and unintended consequences.

Looking Ahead 

AI holds tremendous promise in transforming medicine, healthcare, and life sciences and will continue to surprise us. AI will inevitably play a large part in all future medical breakthroughs in its ability to crack big data, predict potential epidemics, and contribute to the development of innovative therapies and diagnostics tools. Patient outcomes will be better because of AI, provided that the risks and benefits of this tool are carefully weighed. Over the next ten years, more than ever, ethical discussions and the establishment of regulatory frameworks will need to be at the forefront of AI as we see the life science community employ this emerging technology in some shape or form in their research and development of new products and services.

Authored by Maria-Cristina Smith, AVP, Products & Professional Liability Specialist

1 - U.S. Food and Drug Administration (FDA), “Artificial Intelligence and Machine Learning (AI/ML) for Drug Development”, 2023

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