JMIR Publications

ABSTRACT

Background:

Evidence for medication use in pregnancy and lactation is sparse, and clinicians and patients are increasingly encountering large language model (LLM)–generated summaries of drug information. Little is known about how end users perceive such summaries or what they need from them. The Maternal and Pediatric Precision in Therapeutics Knowledge Portal (MPRINT-KP) indexes more than 750,000 maternal and pediatric pharmacology papers, and researchers are developing LLM-generated drug summaries to complement it.

Objective:

Explore the information needs of pregnant and lactating individuals and the clinicians who care for them and identify user-centered design suggestions for LLM-generated drug summaries.

Methods:

We conducted a qualitative descriptive study using semi-structured video interviews (20–45 minutes) with 8 individuals (all pregnant) and 3 clinical providers (a clinical pharmacist, a maternal-fetal medicine physician, and an obstetrician-gynecologist) in February 2026. Participants reviewed a roughly 4000-word, web-formatted LLM-generated summary of methadone use in pregnancy and lactation, synthesized from 52 full-text articles cited in the LactMed methadone record (CASRN: 76-99-3) and commented on its content and format. Transcripts were analyzed using inductive thematic analysis, and findings were organized by the three study aims into six themes, from which we derived design suggestions.

Results:

Safety was ranked as the top concern by 9 of 11 participants; all 8 patients framed safety in terms of risk to the infant or fetus. Most participants cross-checked medication information across several sources. No participant expressed unconditional trust in AI-generated information and non would act on it without human verification. If their clinician endorsed it, 5 of 8 patient would be more receptive to it. Participants perceived the summary as clinician-oriented: At least one unfamiliar term was flagged by 7 of 8 patients, and 7 of 11 judged it too long. Clinicians found the language appropriate but wanted dosing (3/3), indications and risk–benefit framing (2/3), monitoring parameters (1/3), and noted that the summary lacked the clinical context they would give in counseling. Eight of 11 found the in-text blue hyperlinked citations disruptive or confusing and 7 of 11 wanted tables or figures. Six themes emerged across the three aims: information needs and source preferences (Aim 1); safety comes first, and safety means the baby, and conditional trust and triangulation (Aim 2); and content, formatting, and visual aids (Aim 3).

Conclusions:

A single LLM-generated summary could not meet the needs of both patients and clinicians. Design suggestions include audience-specific layers (a plain-language, infant-focused patient version and a clinical version with dosing and monitoring), footnote-style traceable citations, diverse presentations, a short summary at the top, explicit AI-generation and human-review disclosure, and risk–benefit context. For drugs like methadone, where stopping treatment increases maternal risk, a patient-facing layer should pair infant risk with maternal benefit of continuation of treatment. These findings are based on a small sample and a single drug and should be tested with other medications and a larger, more diverse sample.