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3 Modern AI & ML Implications that Could Alter Clinical Trials for Good

AI in Life Sciences-Short

The life sciences industry is rapidly transforming. Advancements in artificial intelligence (AI) and machine learning (ML) technology are reshaping the entire industry and changing today's labor market. Forward-thinking companies are jumping on the ‘AI bandwagon’ to maintain their competitive edge and drive scientific breakthroughs. However, other organizations have been slower to recognize the opportunities the evolving technological landscape presents.

With an AI revolution upon us, I thought it would be helpful to share some implications I've seen so far that are advancing the life sciences industry, particularly clinical trials. AI and ML may even harness the power to change them for good!


3 AI & ML Implications Altering Clinical Trials

There are undeniable advantages to leveraging AI in clinical trials. This technology has the potential to facilitate greater trial participation and enhance the overall efficacy of the trial process. Still, it’s crucial to recognize that some uncertainties lie ahead. Can AI truly expedite trials and speed to market? Can physicians effectively integrate AI into their practices? And what additional benefits might come from having such a wealth of knowledge readily available at our fingertips?

There are also valid reasons for concern when using AI in clinical trials. If inaccurate or flawed data is put in, there’s a risk of generating problematic outcomes. The question on many minds is - can participants consistently and accurately input essential data, such as their blood pressure, weight, or side effects, without supervision? Could bias alter results and outcomes? What other gaps need to be assessed to ensure that AI and ML are utilized effectively in the clinical trial landscape?

Let’s consider some examples of how AI and ML are changing clinical trials.

Implication #1: Clinical Trial Design

The approach to clinical trials is changing as more companies incorporate AI and ML throughout the trial process. According to a whitepaper by the National Institute of Health (NIH), when it comes to reshaping clinical trials, “AI solutions can enable faster and more accurate hypothesis generation and analysis to enhance our understanding of disease evolution, as well as to improve drug discovery, cohort composition, monitoring, adherence, and endpoint selection.”

AI and ML could eliminate the need for physical visits and in-person interactions with coordinators or nurses. Instead, participants can engage remotely using technology to fulfill essential actions. This spans medication intake, injections, or device usage, as well as recording vital signs, side effects, positive effects, and overall impact. AI and ML then analyze the collected data to predict trial outcomes.

New trial designs are leaning on AI and ML to rapidly sift through massive data sets and pinpoint correlations, outliers, and other significant patterns. This is already powering the discovery of new molecules and helping deliver new medicines - way more quickly than before its use. What would’ve once taken years to research, discover, and implement is now happening in a matter of months.



Implication #2: Diversity & Inclusion in Clinical Trials

AI and ML are enhancing diversity, equity, and inclusion (DE&I) in clinical trials. They facilitate wider access to clinical data, reduce barriers to trial participation, and generate outcomes that benefit underrepresented communities. The ‘All of Us’ research program by the NIH serves as a prime example of DE&I in clinical trials. Its objective is to recruit a diverse participant pool to get broader reach for pharmaceuticals, drug discoveries, and medical devices. In turn, the program’s outcomes improve the health and well-being of underrepresented communities.

AI also lowers language barriers in clinical trials and identifies and mitigates biases in trial design and analysis. AI-powered natural language processing (NLP) tools can provide real-time translation services, making it easier for participants who speak different languages to understand study materials and communicate effectively with researchers. It’s also being used to analyze data and identify systemic biases, such as overrepresentation or underrepresentation of certain groups, and provide insights to help researchers address these issues.

These examples merely scratch the surface. It’s clear that AI can potentially alter DE&I within clinical trials for the foreseeable future.



Implication #3: Shifts in the Hiring Market

Finally, the adoption of AI and ML in clinical trials is reshaping the hiring landscape, emphasizing the growing need for a blend of expertise in AI, ML, and life sciences. The inclusion of data science, computational biology, and bioinformatics experts is becoming increasingly critical within research and development teams. These professionals collaborate with traditional lab roles, combining their skill sets to achieve shared goals.

So, what could that mean for companies? If quicker decisions and quicker data increase speed to market, you might need a more flexible workforce or quicker ramps with more people. Access to talent in specialized areas outside the company's expertise could become crucial. However, finding individuals with the right experience, specialties, and a solid background might pose challenges. Another aspect to consider is data integrity. With the widespread adoption of AI and ML, we may even see new jobs emerge focused on verifying data to ensure the reliability of AI-driven insights.




CLOSING THOUGHTS

The implications of AI and ML in clinical trials are endless. The evolving technological landscape is ushering in transformative changes that could permanently reshape the future of the life sciences industry. By leveraging these advancements collectively and responsibly, we can optimize trial processes, enhance patient outcomes, and expedite the discovery of life-saving treatments.

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