Why Healthcare Requires a Different Approach to AI

Almost every company in the pharmaceutical industry now uses AI in some capacity. However, healthcare commercialization carries constraints that most industries do not: regulation, medical accuracy, therapeutic complexity, and a wide range of stakeholders. In this Peer Exchange® episode, Shankar Narayanan, CEO of Real Chemistry, Zoe Lazarra, CMO of Genetech, and Claudia Ciciliano, VP Digital Strategy & Product Operations at Bayer, examine what healthcare-specific AI needs to get right when compared with generic AI tools.

Narayanan frames the issue as a dual challenge. Within pharmaceutical organizations, AI use is a relatively contained problem that calls for the right guardrails and rigorous quality checks. The larger challenge is the general-purpose AI that patients and healthcare professionals use, where the industry has an obligation to help ensure that information about brands and therapies are as accurate as possible.

Lazarra notes that in healthcare, a technically correct answer can still be the wrong answer depending on who receives it. Physicians increasingly use AI in the moment of care, and patients often use it before they reach the doctor’s office. This makes validated, compliant evidence a requirement and audience-specific context essential.

Ciciliano adds a layer of responsibility. She emphasizes the obligation to speak to all patients, remove patient barriers, and use personalization data responsibly. As more marketing becomes automated and teams shift into an oversight role, maintaining that critical eye becomes even more important.