Artificial Intelligence, or “AI,” has been a big buzzword for a reason. Currently, 86% of healthcare providers, life sciences companies, and technology vendors use some sort of AI technology, and the market value for AI in life sciences is currently valued at 2.88 billion dollars. As AI technology continues to make advancements, life sciences companies will increasingly begin to implement AI to expedite drug discovery, optimize clinical development and operations, and enhance the patient experience. Explore the three below 2024 AI trends in the life sciences industry.

2024 AI Trends in the Life Sciences Industry

1. Drug Discovery 

Discovering drugs is a long, iterative process, requiring a vast amount of time and resources, often without any guarantee of eventual success. Only about 10% of drug candidates evaluated in Phase I made it to market. With AI-enhanced technologies, life sciences companies are beginning to expedite the drug discovery process, saving millions of hours of research and minimizing the potential for human error. 

While using AI in drug discovery is not exactly a new trend, with Deep Mind unveiling AlphaFold in 2018, executives at both Deep Mind and Nvidia believe this is a breakthrough moment thanks to the confluence of three things: the massive training data now available, the explosion of computing resources, and the advancements in AI algorithms. 

Being able to predict the shape of a protein based on its amino acid sequences is of intense interest to life science companies. Through deep learning, AI models can be trained on millions of different protein sequences and their underlying structures, helping those models uncover patterns in biology at breakneck speed, including the ability to “write” proteins with new shapes and molecular functions de nova, without starting from proteins found in nature. The implications are vast and exciting, as AI stands to save researchers countless years of manual labor and fruitless experimentation while discovering vital biological connections that may never have been found.  

AI can offer significant benefits not only by pinpointing promising breakthroughs but also by efficiently filtering out potential candidates. According to Boris Braylyan, Vice President and Head of Information Management at Pfizer, one of the smartest decisions for a pharmaceutical company includes choosing which medications not to pursue. A drug that will not be effective enough or will have problematic side effects pulls resources away from developing and delivering medications that could make life better for millions. Using data to make faster decisions on a medicine’s potential allows for the reallocation of resources, dollars, and expertise to the next promising candidate faster. 

AI In Action: Isomorphic Labs 

At the start of 2024, Isomorphic Labs, a digital biology company leveraging AI specifically for drug discovery, announced a strategic partnership with Novartis to discover small molecule therapeutics against three undisclosed targets. Its new iteration of AlphaFold, in concert with Google DeepMind, expands beyond proteins to include small molecules and nucleic acids. 

Novartis believes the partnership will, “accelerate [their] ability to deliver life-changing medicine.” Isomorphic Labs aims to redefine drug discovery by building powerful new predictive and generative models of biological phenomena to help anticipate how drugs will perform and design molecules. Click here for more information on this use case.

2. Smart Data Management in Clinical Trials and Regulatory Processes

Like drug discovery, clinical trials are time-consuming and costly with no guarantee of success. However, generative-AI technologies, when paired with traditional processes, have substantially reduced the need for manpower in tedious tasks by automating several steps in the clinical trial data management process. Moderna has even started using ChatGPT’s analytics tools to optimize vaccine doses for clinical trials, a key determinant in the success or failure of a drug candidate. 

Once protocols have been set up and AI has been customized to the company’s needs, the time it takes to do several of the standard tasks becomes significantly reduced. AI can aid in extracting and categorizing relevant data, establishing connections across disparate sources, autogenerating case reports, performing real-time data review and cleaning, and automatically generating queries based on context and patient information. Moderna has already constructed 750 different “flavors” of large language models to tackle various tasks throughout their company. 

Machine-learning analysis has the potential to improve the quality of regulatory submissions by identifying requests for information that government regulators may have and incorporating the answers from the start. “In the future, we believe that AI may help us predict what queries regulators are likely to come back with,” says Braylyan. “We may then be able to improve our submissions by predicting in advance what regulators are likely to ask and coming prepared with those answers ahead of time,” streamlining the regulatory process and saving weeks of back-and-forth communication with regulators when bringing a drug to market.  

AI in Action: Saama 

Saama, a software company leveraging AI technology for clinical development and commercialization, announced a new multi-year agreement with Pfizer to help them to continue to expedite their clinical trials. The new engagement is an extension of Saama’s initial success with Pfizer, when they worked together on the COVID-19 vaccine. 

The new agreement will continue to scale Saama’s Smart Data Quality (SDQ) to additional portfolios to streamline and accelerate data review processes as well as potentially reduce regulatory submission timelines. Saama uses AI to reduce the time and effort required for data review and reconciliation and to improve the quality and consistency of data. They also aim to streamline statistical programing and biostatistics workflows, digitize study specifications, and generate submission ready TLF artifacts to reduce regulatory submission timelines. Click here for more information on this use case.

3. Enhancing the Patient Experience

AI is not only creating efficiencies in drug discovery and clinical development, but it is also revolutionizing patient care. Oftentimes when a disease or illness is detected determines the likelihood of successful treatment. Therefore, companies are beginning to leverage the power of AI to improve patient outcomes through predictive analytics and diagnostic assistance. 

Healthcare companies are implementing AI technology to analyze medical imaging data, lab results and patient systems, helping to provide real-time insights and assessments, and ultimately accelerating diagnosis and treatment. Utilizing historical patient data in combination with AI helps to identify patterns and predict potential health risks or complications, allowing healthcare providers better opportunities to intervene early and prevent adverse outcomes. 

AI in Action: Viz.ai 

Within the past year, Viz.ai secured backing from Bristol Myers Squibb to develop hypertrophic cardiomyopathy-spotting AI to improve the speed and accuracy of diagnoses. Hypertrophic cardiomyopathy (HCM) causes the heart muscles to become abnormally thick, but it often goes undiagnosed for years because it triggers few outwardly visible symptoms.  

To aid in this, Viz.ai is developing an AI tool that can automatically sift through routine ECGs and send alerts to the appropriate care teams if signs of HCM are detected. Viz.ai is not being used to diagnose the disease, but rather flag patients for additional testing if there are abnormalities. Click here for more information on this use case. 

Looking Ahead 

The burgeoning landscape of AI in the life sciences industry marks a pivotal moment for companies that choose to embrace it, ushering in a new era of innovation and possibility. From revolutionizing drug discovery pipelines to optimizing clinical development processes and elevating patient experiences, AI technologies are reshaping every facet of the industry.  

If you’re interested in exploring the potential of AI in your company, contact us today to learn how Clarkston’s team of data and analytics experts can help build and scale AI solutions across your business.