AI for Commercial

AI for Medical Affairs: Automated Medical Information Responses

ANG Associates
Life Sciences & AI Consulting

Nov 2025 5 min read

The Automation Opportunity in Medical Information

Every medical information (MI) team in a pharmaceutical or biotech company handles a steady stream of unsolicited requests from healthcare providers: dosing in special populations, off-label questions reframed as scientific inquiries, comparative efficacy questions the label does not directly answer. Drafting a compliant, source-anchored response has traditionally meant a medical information specialist searching internal databases, standard response letters, and the literature, then routing the draft through medical and regulatory review. Generative AI is now being tested across the industry to speed the drafting step: pulling relevant standard response documents, summarizing supporting literature, and producing a first-pass answer for a human reviewer to check, edit, and approve. The Medical Affairs Professional Society (MAPS) has highlighted this pattern among its member companies, describing generative AI's use to summarize existing publications and generate scientific response documents, alongside faster literature reviews for MI teams.

The appeal is straightforward: MI inquiry volume is growing, HCPs increasingly expect a fast turnaround, and much of the underlying work (searching, summarizing, formatting to a standard template) is exactly the kind of repetitive, structured task large language models handle well. The risk is equally straightforward: an unreviewed AI draft that strays from approved labeling, overstates efficacy, or answers an off-label question directly is a medical-legal and regulatory problem, not just a quality one.

Guardrails: Keeping Generated Answers Inside the Label

Because MI responses sit close to promotional and regulatory risk, the guardrails matter as much as the model. Industry guidance converging around generative AI in medical affairs generally points to the same set of controls: every AI-generated response is labeled as such and routed through mandatory human review before it reaches an HCP; the system retrieves and cites source documents (the label, approved standard response letters, published literature) rather than generating claims from general model knowledge; low-confidence or off-label queries are automatically escalated to a human specialist rather than answered automatically; and every input, output, and reviewer decision is logged for audit purposes.

Medical information professionals do not own the data they handle, they are custodians of it, and that stewardship principle should guide every decision about how AI is allowed to touch a response before a human signs off on it.

Regulators are beginning to formalize expectations in this space. In January 2025, the FDA published its first draft guidance on AI in regulated drug decision-making, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, which proposes a risk-based, seven-step credibility assessment framework: define the question the model addresses, specify its context of use, assess the risk of that specific use, build and execute a plan to establish credibility, document results, and judge whether the model is fit for that purpose. The framework is written for regulatory submissions, but the same logic, matching the rigor of validation to the risk of the use case, applies directly to an MI response tool that touches HCP-facing content. In the European Union, the AI Act adds another layer of planning: prohibited-practice and AI-literacy obligations took effect in February 2025, general-purpose AI model obligations followed in August 2025, and the broader set of high-risk system obligations becomes applicable from August 2, 2026, which industry advisors are already treating as the practical deadline for AI inventories, risk classification, and human-oversight procedures in life sciences workflows.

What the Evidence Shows

Two independent bodies of evidence help separate genuine capability from hype. On the capability side, a 2024 study published in Digital Health compared GPT-3, GPT-3.5, and GPT-4 against licensed pharmacists on 70 real-world drug information questions. GPT-4 produced completely accurate responses 64.3 percent of the time, described by the authors as comparable to human pharmacists, up from 45 percent for GPT-3.5 and 30 percent for GPT-3. Both GPT-4 and the human pharmacists reached a 95 percent safe-response rate, and GPT-4 actually included proactive risk-mitigation guidance more often than the pharmacists did (70 percent versus 25.7 percent), though pharmacists still outperformed the model on nuanced categories such as pregnancy and lactation questions. The result supports using AI to draft, not to answer unsupervised.

On the adoption side, IQVIA's analysis of physician and clinician behavior found that by the spring of 2025, 54 percent of HCPs were already using generative AI tools to access scientific information, rising to 75 percent among younger clinicians, with 94 percent of users saying it made information easier to find and 72 percent saying it helped them make better treatment decisions. McKinsey's survey of more than 100 pharmaceutical, biotech, and medical-device leaders similarly found broad interest in commercial and medical applications of generative AI, estimating $18 billion to $30 billion in potential annual value across commercial functions industry-wide, out of a wider $60 billion to $110 billion opportunity. Taken together, the data suggests HCPs are already moving toward AI-mediated information, whether or not a given company has built a governed system of its own, which raises the compliance stakes for MI teams rather than lowering them.

How ANG Associates Supports Implementation

Deploying an AI-assisted MI response system is less a model-selection exercise than a validation and governance exercise: mapping the tool against GAMP-style computerized system validation, defining human-in-the-loop checkpoints that satisfy medical-legal review, building the retrieval architecture so every draft cites approved labeling and standard response content rather than free-generated text, and setting up the audit trail a health authority inspection would expect to see. ANG Associates works with life sciences organizations on exactly this intersection: AI strategy framed around a specific, risk-assessed use case rather than a generic pilot, GxP-aligned validation of the resulting system, and the SAFe and Agile delivery discipline needed to get from pilot to a production tool that medical, legal, and compliance functions are all comfortable standing behind.

Sources

medical affairsgenerative AImedical informationGxP compliancepharma AI governance

Interested in this topic?

Let's discuss how we can apply these approaches to your organization.