From friction to focus: How publications teams can use AI today

Lessons from the 2025 ISMPP Academy in Boston

Every publications manager knows the feeling: congress season is looming, insights are scattered across slide decks and CRM notes, authors are slow to respond, and MLR is backed up again.

AI will not replace authors or peer review, but emerging evidence shows it can meaningfully reduce documentation and drafting time while maintaining or improving perceived quality when paired with strong human oversight.¹

How can we find the sweet spot between inefficient manual processes and complete AI takeover? At the recent IMSPP Academy Meeting in Boston, medical affairs teams considered the question “Will AI replace scientific communication professionals or make you irreplaceable?” Monica Moody, Jennifer Ghith, and Valerie Moss led an inspiring session exploring practical applications and skill development that enable efficiency, maintain scientific rigor, and position you as irreplaceable.

Planning: Turning noise into insight

Key takeaways:

• Manual congress and field insights are fragmented and slow to synthesize.
• Strategic planning suffers when priorities and gaps are identified late.
• Teams spend more time wrangling spreadsheets than shaping the plan.

AI‑enabled text‑mining and natural language processing can process thousands of HCP interactions, publications, and real‑world data points to surface themes and unmet needs, shrinking insight‑generation timelines from months to weeks.²

For publications managers, this enables more agile gap analyses and evidence‑based publication plans that stay aligned with evolving clinical practice and medical affairs strategy.³

Development: Accelerating compliant content

Key Takeaways:

• Drafting manuscripts, plain‑language summaries, and congress materials is repetitive and time‑intensive.
• Version chaos and formatting tasks steal attention from scientific storytelling.
• Journal skepticism about AI authorship creates understandable caution.

Reviews of AI in scientific and medical writing show that generative tools can accelerate literature review, outline creation, and first‑draft development while freeing experts to focus on interpretation and message refinement.⁴

Ethics and policy papers emphasize transparency about AI use, validation of all references, strict adherence to Good Publication Practice and ICMJE criteria, and guardrails that teams can embed directly in SOPs and author guidance.⁵

Review: Reducing friction, not rigor

Key Takeaways:

• MLR cycles are a critical path for publication timelines.
• Reviewers spend too much time on references, claims, and formatting checks.
• Late discovery of unsupported statements leads to rework and delays.

Regulatory and commercial case analyses report that AI‑supported workflow redesign can cut regulatory or compliance‑heavy review timelines by 30–60% through automated pre‑checks of claims, label alignment, and reference mapping, while preserving full auditability.⁶

This shifts reviewer effort toward scientific balance, patient safety, and ethical considerations, without reducing scrutiny.⁷

Governance: Making AI usable and safe

Key Takeaways:

• Many organizations lack clear, practical policies for AI in publications.
• Medical communications teams are caught between expectations to innovate, and “do not use AI” messages.
• Without structured business cases, pilots stall and trust erodes.

Surveys of life‑sciences leaders highlight security and privacy concerns, unclear regulatory guidance, and lack of AI expertise as top barriers to broader adoption, despite high enthusiasm for AI’s potential.⁸

High‑performing organizations respond by defining specific, low‑risk use cases; implementing human‑in‑the‑loop oversight; and tracking KPIs such as cycle time, rework rates, and error reduction to demonstrate value and reassure stakeholders.⁹

Turning friction into focus

Publication teams do not have to leap from today’s manual, spreadsheet‑driven reality to fully automated AI workflows in one step. Thoughtfully targeted use cases, such as faster insight synthesis, draft generation from approved sources, and pre‑MLR checks can strip out friction while preserving scientific rigor, human authorship, and journal compliance. By planning governance, data boundaries, and change management up front, organizations can safely fill in the gap between “no AI” and “all AI,” moving toward a future where publications professionals spend less time on mechanics and more time shaping impactful science.

If you are being asked “What’s our AI plan for publications?” but lack a clear pathway, Woven Health Collective can help you map AI into your existing SOPs, build evidence‑based business cases, and design compliant pilots that make your publications workflow faster, safer, and more impactful. Get in touch with Woven to start your AI‑enabled publications roadmap.

References

1. Tai-Seale M, Baxter SL, Vaida F, et al. AI-Generated Draft Replies Integrated Into Health Records and Physicians’ Electronic Communication. JAMA Netw Open.2024;7(4):e246565. doi:10.1001/jamanetworkopen.2024.6565‌
2. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal. 2021;8(2):188-194. doi:https://doi.org/10.7861/fhj.2021-0095
3. Singh S, Kumar R, Maharshi V, et al. Harnessing Artificial Intelligence for Advancing Medical Manuscript Composition: Applications and Ethical Considerations. Cureus. Published online October 17, 2024. doi:https://doi.org/10.7759/cureus.71744
4. Davide Ramoni, Cosimo Sgura, Luca Liberale, Fabrizio Montecucco, Ioannidis JPA, Carbone F. Artificial intelligence in scientific medical writing: Legitimate and deceptive uses and ethical concerns. European Journal of Internal Medicine. Published online July 1, 2024. doi:https://doi.org/10.1016/j.ejim.2024.07.012
5. Mennella C, Maniscalco U, Pietro GD, Esposito M. Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon. 2024;10(4):e26297. doi:https://doi.org/10.1016/j.heliyon.2024.e26297
6. Gochnauer G. The future of AI in Medical, Legal, and Regulatory (MLR) Revie. Vodori.com. Published September 29, 2025. Accessed December 5, 2025. https://www.vodori.com/blog/the-future-of-ai-in-medical-legal-and-regulatory-mlr-review
7. Kurne S. Part 2 - Accelerating the Medical, Legal and regulatory (MLR) review leveraging AI. pharmaphorum. Published November 27, 2024. Accessed December 5, 2025. https://pharmaphorum.com/digital/part-2-accelerating-medical-legal-and-regulatory-mlr-review-leveraging-ai
8. The Life Sciences Industry Is in Flux; 94% of Leaders See AI Agents as Stabilizer. Salesforce. Published September 25, 2025. Accessed December 5, 2025. https://www.salesforce.com/news/stories/life-sciences-ai-survey-insights-2025/
9. Ammanath B. Kulkarni V. Ritter C., AI trends: Adoption barriers and updated predictions Deloitte US. Deloitte. Published September 15, 2025. Accessed December 5, 2025.
   https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/blogs/pulse-check-series-latest-ai-developments/ai-adoption-challenges-ai-trends.html