Today's AI developments span surgical diagnostics, mammography screening, and clinical operations. Six key updates show how AI is moving from pilot projects to structured implementation across healthcare. September 29, 2025 brings major guidance from regulators and promising results from clinical studies.
Joint Commission releases first AI governance guidance with certification coming
Harvard's PICTURE AI achieves >98% accuracy in brain tumor surgery
$16M PRISM trial tests AI as radiologist co-pilot in mammography
AI automates clinical trial recruitment with ChatGPT integration
AI identifies high-risk pediatric asthma subgroup in EHR analysis
AI standardizes melanoma biomarker assessment with superior consistency
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Joint Commission Issues First AI Governance Guide
CHAI and the Joint Commission released "Responsible Use of AI in Healthcare" to guide health systems adopting AI tools. The framework requires formal AI governance with a named leader and policies covering procurement, safety, risk management, and HIPAA compliance. Source
The guidance calls for risk-based local validation and ongoing quality monitoring. Health systems must disclose AI use to patients and obtain consent when AI affects care. Organizations should use voluntary safety reporting systems like the CHAI Health AI Registry. Source
More detailed playbooks and a voluntary Joint Commission AI certification are coming next. This guidance sets basic standards that will shape vendor negotiations, monitoring budgets, and patient transparency practices.
Harvard AI Achieves >98% Accuracy in Brain Tumor Surgery
Harvard researchers developed PICTURE, an AI system that distinguishes glioblastoma from primary CNS lymphoma during surgery with over 98% accuracy. The tool provides real-time diagnostic support in the operating room. Source
PICTURE can reduce OR time and pathology delays by providing faster intraoperative decisions. The system may reduce reliance on frozen sections in some cases. Teams should validate the tool locally and plan OR integration workflows before clinical use.
$16M PRISM Trial Tests AI in Mammography Screening
UCLA and UC Davis will lead PRISM, a randomized trial testing AI support for mammogram interpretation. The $16 million PCORI-funded study will analyze hundreds of thousands of mammograms across seven academic centers in California, Florida, Massachusetts, Washington, and Wisconsin. Source
The trial uses Transpara for image analysis and Aidoc aiOS for workflow management. Radiologists make final decisions in all cases. The study will measure cancer detection rates, recall rates, and patient and radiologist trust through surveys and focus groups.
PRISM aims to provide robust real-world evidence on AI impact in screening. Results will guide coverage decisions, technology adoption, and clinical workflows across the field.
ChatGPT Speeds Clinical Trial Recruitment
Clinical teams report using ChatGPT to accelerate trial recruitment by automating routine tasks like message drafting and answering candidate questions. The tool reduced staff time on communications while keeping clinicians in control of eligibility and consent decisions. Source
Faster screening and outreach can shorten enrollment windows and reduce operational costs. Teams should maintain human review of all AI outputs and address privacy rules before deployment. Continuous monitoring for bias and accuracy remains essential.
AI Identifies High-Risk Pediatric Asthma Subgroup
Researchers used natural language processing on Mayo Clinic EHRs to identify children with asthma at higher risk for pneumonia and influenza. The study followed 22,370 children with median follow-up of 9.8 years. Source
Children positive for both PAC and API algorithms showed 2-3.5 times higher exacerbation risk compared to other asthma subgroups. The NLP method achieved 93-100% accuracy for acute respiratory illness detection. Source
Health systems can use these EHR-based markers to identify high-risk children for targeted prevention programs, vaccine outreach, and resource planning.
AI Standardizes Melanoma Biomarker Assessment
AI-based image analysis for counting tumor-infiltrating lymphocytes in melanoma shows superior consistency compared to manual pathologist scoring. The automated system demonstrates strong prognostic value and supports scalable measurement across laboratories. Source
Standardized TIL counts reduce subjective variation and make biomarkers more reliable for trials and clinical decisions. The automation speeds throughput and lowers labor costs per case. Teams should require local validation before clinical use and engage regulators early for trial endpoints.