The National Quality Forum (NQF), an affiliate of the Joint Commission, is seeking comments on its draft report on the use of "high stakes" AI methods in healthcare quality measurement.
Dated September 8, 2025, the 70-page report addresses a gap in governance and guidance documents and includes strategies to facilitate AI-enabled quality measures in accountability programs, including value-based payment, public reporting, performance-based provider network designs, and accreditation, according to NQF.
The report's authors regarded these methods as "high stakes" accountability applications that could be publicly reported and used for payment decisions, among other purposes.
"The landscape of AI in quality measurement is likely to remain uneven, not only across measured entities, but also across different types of quality measures depending on how interdisciplinary or discipline specific they are," the authors noted in the report. "For example, the use of AI in radiology may be more common than other specialties."
A multistakeholder technical expert panel (TEP), funded by the Gordon and Betty Moore Foundation, developed the roadmap. The guidance will interpret consensus-based frameworks on the use of AI in healthcare for the use case of quality measurement, the NQF emphasized.
However, as AI methods evolve, the initiative may eventually inform broader applications beyond accountability programs, such as quality improvement and clinical decision support, the NQF added.
Comments on the draft report are due by October 15, 2025, and plans are to publish final guidance in spring 2026.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
