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Over the past several months, I’ve noticed a marked shift: life sciences organizations -- especially insights, analytics, and commercial strategy teams in our neck of the woods -- are looking far more closely at how artificial intelligence will integrate into physicians’ decision-making and everyday workflows. While AI has been on the industry’s radar for years, 2024 and early 2025 have brought a new urgency as companies move from curiosity to formal insight-building.
This renewed focus also makes it a good moment to revisit an important policy document that, candidly, many in biopharma overlooked when it was published a year ago: the American Medical Association’s Principles for Augmented Intelligence Development, Deployment, and Use (November 2024).
For those who want to explore the full text, the document is publicly available here: https://www.ama-assn.org/system/files/ama-ai-principles.pdf (Note: This is the AMA’s published link to the report.)
As biopharma organizations accelerate their own AI pilots, from generative medical content to predictive analytics to next-best-engagement engines, it is increasingly important to understand the guidance physicians themselves are receiving from their professional societies. These policy signals shape expectations, trust, skepticism, and day-to-day behavior.
After reviewing the AMA’s 2024 principles with this lens, five themes rise to the top for commercial, insights and analytics leaders in pharma and biotech.
The AMA sets a clear tone: AI used in health care must be ethical, equitable, transparent, and grounded in reliable evidence. As physicians adopt clinical and administrative AI tools, they’re being coached to ask deeper questions about model provenance, data quality, bias, and real-world performance.
For biopharma, this means that AI-powered customer engagement, content generation, and analytics will be evaluated through a more critical lens. Tools that cannot explain their rationale, or that seem opaque, will face resistance. Transparency and validation will become frontline differentiators.
A major policy emphasis is the alignment of liability with the entities best positioned to know and mitigate AI risks. This often means developers, vendors, or organizations mandating the use of AI systems.
For biopharma, the takeaway is thus: when deploying AI-generated medical information, analytics, or educational resources, responsible design and documentation will matter. Physicians will look for reassurance that engaging with AI-enabled content does not inadvertently shift risk onto them.
The AMA’s report highlights widespread concerns about payor use of automated decision-making tools, especially those contributing to inappropriate or rapid denials of care. These experiences are creating a climate of caution -- and sometimes frustration -- among HCPs.
This backdrop matters for pharma insights work. When testing AI-assisted engagement concepts, remember that physicians may not differentiate between “good AI” and “bad AI” without clear context. Their skepticism is often born not from clinical tools but from administrative algorithms that undermine patient access. Messaging and design must counteract this fatigue.
The AMA underscores that physicians should understand, and explain to patients, how AI handles personal data. It emphasizes opt-out rights, disclosure requirements, and protections against reidentification.
As biopharma develops AI-enabled patient support tools, digital biomarkers, and real-world data platforms, expectations around privacy-by-design will only grow. Insights and analytics teams should anticipate heightened physician scrutiny of how patient data informs AI-driven recommendations or content personalization.
A defining message in the AMA’s principles is the importance of physician involvement in AI design, deployment, and evaluation. Tools must align with clinical workflows, reduce administrative burden, and meaningfully contribute to patient care.
For biopharma, this is a strong signal to prioritize co-creation. Whether developing AI-driven educational resources, predictive engagement models, or medical insights dashboards, early and continuous clinician input will determine adoption. Tools must feel like enablers, not disruptors.
Yes, this analysis may be a year late -- but the timing is right. Biopharma organizations are now moving beyond experimentation and beginning to integrate AI more deeply across medical and commercial operations. As that happens, understanding how physicians are being guided to evaluate these technologies becomes mission-critical.
The AMA’s 2024 AI principles are not just policy; they are a roadmap for physician expectations. For insights professionals, they provide a valuable lens through which to interpret emerging behaviors, anticipate adoption barriers, and design AI-powered solutions that clinicians can trust.
