State of clinical AI in 2026.
Authors
Affiliations (18)
Affiliations (18)
- The University of Arizona College of Medicine Phoenix, Phoenix, Arizona, USA.
- Stanford Department of Medicine, Stanford, Stanford, California, USA.
- Radiation Oncology, Stanford University, Stanford, California, USA.
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
- Department of Medicine, New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Dermatology, Harvard Medical School, Boston, Massachusetts, USA.
- Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
- Medical University of South Carolina, Charleston, South Carolina, USA.
- Massachusetts Institute of Technology Institute for Medical Engineering & Science, Cambridge, Massachusetts, USA.
- Division of Neurology, University of Alberta, Edmonton, Alberta, Canada.
- Department of Internal Medicine, Cambridge Health Alliance, Cambridge, Massachusetts, USA.
- Hospital Medicine, Stanford University School of Medicine, Stanford, California, USA.
- The Institute of Medical Education Research Rotterdam, Rotterdam, The Netherlands.
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
- Stanford Division of Computational Medicine, Stanford University, Stanford, California, USA.
- Stanford Clinical Excellence Research Center, Stanford University, Stanford, California, USA.
- Division of Hospital Medicine, Department of Medicine, Stanford University, Stanford, California, USA.
Abstract
Clinical artificial intelligence (AI) has advanced rapidly, with frontier large language models now matching or exceeding physician performance on simulated diagnostic reasoning and clinical decision-support tasks. Yet adoption has outpaced the evidence base: fewer than 5% of cleared U.S. Food and Drug Administration (FDA) AI/machine learning (ML) devices have undergone peer-reviewed evaluation and prospective randomised trials remain scarce. This narrative review synthesises evidence from ARISE's State of Clinical AI Report 2026 across six domains: model performance and human benchmarking, evaluation methodology and benchmark validity, foundational methods including multimodal and multiagent systems, clinical workflow integration, patient-facing applications and applied domain-specific AI. We conducted a targeted literature search of PubMed, medRxiv and arXiv supplemented by expert nomination, including empirical studies of AI in clinical contexts. Across domains, frontier models demonstrate strong clinical reasoning capabilities but exhibit persistent failures in uncertainty calibration, metacognition and robustness to distributional shift. Standard benchmarks have saturated, prompting development of multidimensional evaluation frameworks that assess safety, real-world workflows and agentic capabilities. New trends in foundational methods include converting medical data into tokens and developing multiagent, multimodal systems. In clinical workflows, early prospective trials in clinical decision support and diagnostic imaging show promising results, though human-AI collaboration remains suboptimal and risks of automation bias and clinician deskilling are emerging. In tandem, patient-facing AI is progressing with more personalised health assistance; with this comes the risk of patient overtrust, raising the bar for guardrails. The most credible translational progress occurs on narrowly defined tasks with clear endpoints, while broader clinical autonomy awaits advances in evaluation and human-AI interaction.