Why AI tools matter in Medical Affairs
The use of AI-enabled tools in Medical Affairs has become one of the most discussed topics in pharmaceutical communications. After two years of active research, testing, and real-project implementation, our team has developed a practical framework for selecting and deploying AI tools in scientific and medical workflows.
This article is not a theoretical overview. It is a practical guide based on our daily experience — tools we use, tools we’ve tested and rejected, and the criteria that separate genuinely useful solutions from marketing noise.
Key insight: The most effective AI tools in Medical Affairs are those that accelerate specific workflows — literature search, claim verification, data extraction — rather than promising to “replace” medical writers or scientists.
Tool categories we evaluate
We organize AI tools for scientific work into six functional categories, each addressing a specific bottleneck in the medical communications workflow:
| Category | Primary use case | Example tools |
|---|---|---|
| Literature search | Structured PubMed queries with AI-generated summaries | Evidence Scanner Research, Consensus, Elicit |
| PDF analysis | Batch processing of clinical papers with custom questions | Evidence Scanner Snapshots, SciSpace |
| Literature monitoring | Automated weekly digests by drug, target, or topic | Evidence Scanner Monitoring, Semantic Scholar |
| Claim verification | Cross-referencing promotional claims against source docs | Evidence Scanner Fact-Checker |
| AI-Enhanced EDC | Advisory board transcription + structured summaries | Evidence Scanner AI-Enhanced EDC |
| Data capture | Electronic data collection for registries and RWE | Evidence Scanner EDC Platform |
How we select tools: our evaluation criteria
Not every AI tool that appears on Product Hunt or in a LinkedIn post is worth integrating into a medical communications workflow. We’ve developed a set of practical criteria:
- Source transparency — does the tool show which papers or sources it used to generate the answer?
- Medical accuracy — have outputs been validated against known correct answers in our therapeutic areas?
- Workflow integration — can we connect this tool to our existing processes without rebuilding everything?
- Data privacy — where is the data stored? Is it GDPR-compliant? Does the vendor use inputs for model training?
- Speed vs. quality trade-off — does the speed gain justify the review overhead required?
We don’t build AI tools to replace medical writers. We build infrastructure to remove bottlenecks from their workflow.
— Yakov Pakhomov, Medical Director, MAGLiterature search and monitoring
Traditional PubMed searches require expertise in Boolean operators and MeSH terms. AI-powered alternatives now allow natural language queries with structured outputs — narrative summaries, comparison tables, or endpoint extraction formats.
Our Evidence Scanner Research module processes queries like “compare MACE outcomes across GLP-1 RA cardiovascular outcome trials published after 2020” and returns structured evidence tables with full citations. The monitoring module then tracks these topics weekly, delivering curated digests directly to project teams.
What works
Structured queries with defined therapeutic area, endpoint focus, and time boundaries produce the most reliable results. Open-ended “tell me about” queries consistently underperform.
What doesn’t
AI tools that claim to “read all papers” without showing their search methodology are unreliable for regulatory-grade work. Always verify the search strategy and source list.
Claim verification and MLR readiness
One of the highest-impact applications of AI in medical communications is automated claim verification. Before any promotional or medical material enters the MLR review cycle, every factual claim should be cross-referenced against its stated source.
Our Fact-Checker module processes slide decks, manuscripts, and training materials — flagging claims that lack source support, have outdated references, or contain numerical discrepancies. In one recent project, it identified 18 unsupported claims in a 40-page product deck before the first MLR submission.
Result: Teams using pre-submission AI verification report up to 60% fewer MLR rejection cycles, saving 2–3 weeks per material on average.
Practical recommendations
Based on two years of implementation across multiple therapeutic areas and clients, here are our key recommendations for pharma teams considering AI integration:
- Start with one workflow. Don’t try to AI-enable everything at once. Pick the bottleneck — usually literature review or claim verification — and prove value there first.
- Validate before trusting. Run parallel processes (AI + manual) for the first 3–5 projects. Compare outputs. Build confidence in accuracy before scaling.
- Keep humans in the loop. AI accelerates structure and speed. Expert judgement handles scientific interpretation and MLR readiness.
- Document your workflow. Every AI-generated output should have a traceable path from query to source to validated result.
- Review vendor data policies. GDPR compliance, data residency, and opt-out from model training are non-negotiable for pharma work.
