Using AI to Improve Decision-Making in Biotech: Insights from MindMed’s Chief Medical Officer

Dr. Daniel Karlin is the Chief Medical Officer at MindMed, where he brings unparalleled expertise to the intersection of healthcare and technology. Having previously led clinical, informatics, and regulatory strategy for Pfizer’s Digital Medicine and Innovation Research Lab, he recognizes the immense potential of AI in accelerating treatment development and enhancing patient safety.

Dr. Karlin firmly believes that AI should serve as a supplement — rather than a replacement — to human decision-making in biotech. Moreover, he advocates for putting ethical considerations at the core of all technological applications in medical research and drug development, to ensure that patient well-being always remains intact.

In a recent installment of our “Data Driven Biotech” series by AppliedXL, Dr. Karlin shares his views on best practices for adopting AI and how industry leaders can harness its power to drive innovation. His ideas shed light on the immense opportunities that lie ahead in using AI to reshape biotech.

What do you think are some of the most exciting uses of AI and data in biotech, and why? (e.g. new drug discovery, competitive intelligence, identifying new business opportunities, making winning bets etc.)

“Machine learning, AI, and other forms of advanced digital measurement and data analysis aim to unlock a new generation of collaborative development and care between drug developers, healthcare providers, and patients. These new technologies and treatment approaches, ultimately, should enable us to more quickly and more reliably match a patient with the treatment option most likely to improve their quality of life. Starting at the drug discovery and development phases, better measurement tools can increase the precision of diagnosis, reduce uncertainty around enrollment and clinical outcomes, de-risk development, and increase the speed at which novel therapeutics reach patients in need.”

What are some of the big challenges the biotech industry faces when working with large amounts of data, and how can the industry address these potential hurdles?

“Despite ongoing and recently accelerating advances, the difficulties intrinsic in treatment development, the need to ensure patient safety, and the enormous complexity of healthcare, causes it to lag behind many other industries in effectively working with the massive amounts of data at our disposal. The cross-disciplinary knowledge required to create and deploy digital medicine tools requires continuous input from people across many fields. Organizations that seek to advance this work responsibly need to recognize that this is not a business of shortcuts, quick fixes, or miracle cures. We remain in pursuit of more effective and patient-centric treatments, and digital technologies are now one of our most important tools toward meeting that objective.

Another common barrier to this work is simply agreeing on a shared framework of language and ideas within which to collaborate. So it remains critical for the industry to align on and amplify the core concepts, meta-concepts, and nosologies within digital medicine, bringing them ever closer to the shared vocabulary of healthcare stakeholders.”

What impact do you think AI and automation tools including AppliedXL play in supporting decision making? And what is the role of human expertise, with the increasing use of AI and data?

“In healthcare, expanded access to AI and automation tools should be thought of as a way to empower decision-making based on human expertise, not to circumvent it. Researchers, providers, and patients all tend to agree on the need for more evidence-based approaches to treatment, but often disagree about what constitutes appropriate evidence, and how best to apply evidence to diagnosis and treatment decisions. It just so happens that finding the most reliable evidence, and making sense of it all, has become a more laborious pursuit than past generations could have imagined. All relevant stakeholders share a responsibility to promote ethical medical research and drug development. In the application of technology to this work, we cannot simply outsource ethics and hope for the best.”

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