AI in Healthcare: reporting guidelines

The rapid rise of artificial intelligence (AI) in healthcare is reshaping clinical workflows, patient monitoring, and evidence based decision making faster than almost anyone predicted. It’s no surprise, then, that the scientific literature on the topic has grown just as quickly and with that growth has come a pressing question: how do we make sure the data behind these AI systems is reproducible, safe and trustworthy?

Beneath this explosion of research lie some deeper, structural problems. Biased datasets, opaque black box models, and inconsistent evaluation methods keep chipping away at trust in clinical AI. Together, these issues point to one unavoidable conclusion: the field urgently needs reporting guidelines rigorous enough to guarantee methodological soundness, safety and reproducible results.

Vinay et al. (1) conducted a systematic review that maps and critically appraises 27 AI‑specific reporting guidelines published between 2020 and 2025, revealing substantial variation in their methodological quality. The analysis highlights issues such as inherent time‑lag bias embedded in the earliest frameworks.

In conclusion, given the rapid evolution of AI, reporting standards must remain dynamic — continuous updating and harmonization of criteria are essential to keep pace with the growing complexity and speed of AI integration in healthcare.

 

Author(s): David Ramirez Moro, Biomedical Research Institute of Malaga and the Nanomedicine Platform

Links: 

1.- Vinay V, Jodalli P, Chavan MS, Satyarup D, Bhor K, Buddhikot C. Mapping and Quality Appraisal of Artificial Intelligence Preferential Reporting Checklists, Items, Guidelines, and Consensus in Healthcare: An Altmetric, Bibliometric, and Systematic Review. Int J Dent. 2026 Apr 18;2026:6730710. doi: 10.1155/ijod/6730710. PMID: 42007431; PMCID: PMC13091008.

Keywords: #ArtificialIntelligenceHealthcare #ReportingGuidelines #Reproducibility #AIBiasTransparency #MethodologicalQuality