Why Generative AI Trials Fail the Reproducibility Test

A 2026 systematic review in the Journal of medical Internet research reveals critical reporting gaps in randomized controlled trials of generative AI. Maya and Sam unpack why current reporting standards fail to capture the mechanics of large language models, leaving major blind spots in model versioning, prompt engineering, and input data quality.

Key points

• A review of 74 trial reports evaluating generative AI found that 0% detailed how they handled poor-quality or unavailable input data.
• Only 23.0% of reports specified the exact version of the AI algorithm used, while just 27.0% explicitly reported the system prompt.
• Current reporting standards like CONSORT-AI were built for conventional, locked AI models and suffer from structural mismatches when applied to conversational AI.
• Trials where laypeople were the intended users reported how outputs contributed to decision-making at a drastically lower rate (15.8%) than clinician-facing trials (100%).
• Failing to report inference settings, system prompts, and access modalities makes reproducing generative AI trials virtually impossible for healthcare buyers.

Source: Reporting Gaps and Challenges in Randomized Trials of Generative AI for Medical Use: Insights From a Systematic Review - Journal of medical Internet research, 2026 (CC BY)

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This episode is an AI-generated conversation summarising a public document; the hosts' voices are synthetic. It is for information only and is not medical advice. Always refer to the original source.

Authors: Jae Eun Ko, Ji Woo Hwang, Sung Min Kim, Ji Yean Kwon · DOI: 10.2196/94543

Full transcript: https://ai-in-medicine-podcast.vercel.app/episodes/why-generative-ai-trials-fail-the-reproducibility-test-828fd6