Track. Native AI in Healthcare – Enterprise. tracxn.com https://tracxn.com/d/sectors/native-ai-in-healthcare/__Tl5eFsTO1TeAyMWAv1mZ2ckAIl4ywCTh_vx2THnj_dY/companies (2026).
Nguyen TD Introduction of artificial intelligence in other medical fields. JAMA Health Forum 6e255029 (2025).
Wu, K. et al. Characterizing clinical adoption of medical AI devices through U.S. insurance claims. NEJM AI 1AIoa2300030 (2024).
Poon, EG, Lemak, CH, Rojas, JC, Guptill, J. & Classen, D. Deploying artificial intelligence in health care: A survey of health system priorities, successes, and challenges. J.Am. Medicine. Let me know. Associate Professor 321093–1100 (2025).
Tveit, J. et al. Automatic interpretation of clinical EEG using artificial intelligence. JAMA New Roll. 80805–812 (2023).
Mancila, D. et al. Generalizability of EEG interpretation using artificial intelligence: An external validation study. epilepsy 653028–3037 (2024).
U.S. Food and Drug Administration. Marketing Submission Recommendations for Predefined Change Control Plans for Software Features of Artificial Intelligence-Enabled Devices: Guidance for Industry and Food and Drug Administration Staff. F.D.A. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence (2025).
european union. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC (text with EEA relevance). EUR https://eur-lex.europa.eu/eli/reg/2017/745/oj/eng (2017).
Kull, M. & Annunziata, A. A groundbreaking partnership between Cleveland Clinic and IBM Research. IBM https://research.ibm.com/blog/cleveland-clinic-ibm-discovery-accelerator (2021).
Anastasijevic, D. Mayo Clinic selects Google as strategic partner for healthcare innovation and cloud computing. mayoclinic.org https://newsnetwork.mayoclinic.org/Discussion/mayo-clinic-selects-google-as-strategic-partner-for-health-care-innovation-cloud-computing/ (2019).
european union. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 establishes harmonized regulation on artificial intelligence and introduces Regulation (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139. Fix it. and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Law) (EEA-related texts). EUR https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng (2024).
Medicines and Healthcare Products Regulatory Agency. Health authority exemption for general medical devices. british government https://www.gov.uk/government/publications/health-institution-exemption-for-general-medical-devices (2025).
Zhou, J. et al. Large-scale language models in biomedicine and healthcare. npj artif. intelligence. https://doi.org/10.1038/s44387-025-00047-1 (2025).
UK NHS. Guidance on the use of AI-enabled ambient scribing products in healthcare and care settings. NHS https://www.england.nhs.uk/long-read/guidance-on-the-use-of-ai-enabled-ambient-scribing-products-in-health-and-care-settings/ (2026).
U.S. Food and Drug Administration. Good machine learning practices for medical device development: A guide. F.D.A. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles (2025).
U.S. Food and Drug Administration. Artificial Intelligence-Enabled Device Software Capabilities: Lifecycle Management and Marketing Submission Recommendations – Draft Guidance for Industry and Food and Drug Administration Staff. F.D.A. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing (2024).
National Institute for Healthcare Excellence. NICE Health Technology Assessment: Manual. nice https://www.nice.org.uk/process/pmg36/chapter/introduction-to-health-technology-evaluation (2026).
American Medical Association. CPT (Current Procedural Terminology). amateur https://www.ama-assn.org/practice-management/cpt (2025).
Fornell, D. Radiology dominates among FDA-approved AIs, but reimbursement lags far behind. radiologybusiness.com https://radiologybusiness.com/topics/artificial-intelligence/radiology-dominates-fda-cleared-ai-reimbursement-lags-far-behind (2025).
Basu M. Dozens of AI disease prediction models trained on suspicious data. nature https://doi.org/10.1038/d41586-026-00697-4 (2026).
Ewen, JB et al. GREENBEAN Checklist for Reporting Studies Evaluating the Efficacy of EEG-Based Biomarkers. Clin. Neurophysiology. 1762110777 (2025).
Moons, KGM et al. PROBAST+AI: A modern quality, risk of bias, and applicability assessment tool for predictive models using regression or artificial intelligence techniques. BMJ 388e082505 (2025).
Dunn, J. et al. SzCORE: The seizure community’s open source research evaluation framework for validating automatic electroencephalography-based seizure detection algorithms. epilepsy 6614–24 (2025).
Bilot, B. et al. SynthSeg: Segment brain MRI scans of arbitrary contrast and resolution without retraining. medicine. Anal image. 86102789 (2023).
Goebl, P. et al. Repurposing clinical MRI archives for multiple sclerosis research can yield new insights from old scans. nut. common. 163149 (2025).
Kraljevic, Z. et al. Foresight – A generative pre-trained transformer for modeling patient timelines using electronic medical records: A retrospective modeling study. lancet digit. health 6e281–e290 (2024).
Jiang, Lee et al. Health system-wide language models are versatile predictive engines. nature 619357–362 (2023).
Sheikh, S. & Jehi, L. A predictive model for epilepsy outcome. car. opinion. New roll. 37115–120 (2024).
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Analyzing racial bias in algorithms used to manage population health. science 366447–453 (2019).
Observational health data science and informatics. Standardized data: OMOP common data model. OHDSI https://www.ohdsi.org/data-standardization/ (2026).
Shenzhang, S., Möller-Grell, N., Jiang, ZJ, Dobson, R. & Chandrabalan, VV OMCP: Model Context Protocol Server for the OMOP Common Data Model. HDR https://ukhealthdata.org/wp-content/uploads/2025/09/OMCP.pdf (2025).
Health Level Seven International. HL7 Messaging Standard Version 2.9: Application Protocol for Electronic Data Exchange in Healthcare Environments. HL7 https://www.hl7.org/implement/standards/product_brief.cfm?product_id=516 (2019).
Health Level Seven International. HL7 FHIR Release 5 (R5), v5.0.0. HL7 https://hl7.org/fhir/R5/ (2023).
Möller-Grell, N., Shenzhang, S., Jiang, ZJ, Chandrabalan, VV & Dobson, R. Agent Conversation for OMOP CDM: OMCP-A2A Basic Library. OHDSI https://www.ohdsi.org/2025showcase-310/ (2025).
Tidy, J. Flaw in AI coding platform could lead to BBC reporter being hacked. BBC News https://www.bbc.co.uk/news/articles/cy4wnw04e8wo (2026).
Greenberg, A. Thousands of vibe-coded apps expose corporate and personal data to the open web. wired https://www.wired.com/story/thousands-of-vibe-coded-apps-expose-corporate-and-personal-data-on-the-open-web/ (2026).
Steiger M. Patientendaten stehen nach «Vibecoding» mit KI offen im Internet [Patient data exposed on the internet after “vibe coding” with AI]. SL https://sreigerlegal.ch/2026/03/31/vibe-coding-patientendaten/ (2026).
Holdgraf, C. et al. iEEG-BIDS extends the specification of brain imaging data structures to human intracranial electrophysiology. Science. data 6102 (2019).
Kaur H et al. E-188 Impact of artificial intelligence tools on enhancing patient follow-up compliance for unruptured intracranial aneurysms: Insights from Viz Aneurysm® software. J. Neurointerv. Surgery. https://doi.org/10.1136/jnis-2024-SNIS.293 (2024).
Zhang, X. et al. Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischemic stroke (GOLDEN BRIDGE II): A cluster randomized clinical trial. BMJ 392e085810.
