From code to care in hours: Why "go fast and fix things" is the new medical imperative
Abstract
Recent technological advances in artificial intelligence and machine learning have led to rapid changes in clinical informatics. With the release of new models, the barrier to creating medical software has markedly decreased. As noted in the now-viral article by Matt Shumer, we have moved from a world where building a clinical application took a team of engineers working over the course of months to years, to one where a single clinician can develop and deploy a working prototype in hours to days. However, the same innovation driving this 'explosion' of tools also carries the risk of flooding health systems with unvalidated 'AI slop.' As prohibiting tools developed through AI runs the risk of stifling innovation and limiting potentially impactful discoveries, we suggest abandoning the slow, fear-based resistance to AI adoption. Further, we propose a 'go fast and fix things' framework-utilising target product profiles, regulatory sandboxes, and continuous audit-to harness the speed of generation and prototyping while rigorously validating clinical utility.
Read the paper
Related studies
- Regulation of nucleolus assembly by non-coding RNA polymerase II transcripts
- For a Healthy Histone Code, a Little SUMO in the Tail Keeps the Acetyl Away
- Discovering a binary CTCF code with a little help from BORIS
- Article Commentary: Stabilizing the Code–-Methods to Preserve RNA Prove Their Worth
- Foreword to the Special Issue for the RINTC (The Implicit Seismic Risk of Code-Conforming Structures) Project
- Commentary: Promoting Health and Well-being in Pediatric Primary Care Settings: Using Health and Behavior Codes at Routine Well-child Visits
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.