REFERENCES

1. Mata v. Avianca, Inc., 678 F. Supp. 3d 443 (S.D.N.Y. 2023). Available from: https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1%3A2022cv01461/575368/54/. [Last accessed on 2 September 2026].

2. Gumbs AA, Perretta S, d’Allemagne B, Chouillard E. What is Artificial Intelligence Surgery? Art Int Surg. 2021;1:1-10.

3. Capelli G, Verdi D, Frigerio I, et al. ; Artificial Intelligence Surgery Editorial Board Study Group on Ethics. White paper: ethics and trustworthiness of artificial intelligence in clinical surgery. Art Int Surg. 2023;3:111-22.

4. World Health Organization. Ethics and governance of artificial intelligence for health. Available from: https://www.who.int/publications/i/item/9789240029200. [Last accessed on 2 September 2026].

5. Grasso SV, Spolverato G, Capelli G, et al. The role of advanced technologies and artificial intelligence (AI) in surgical training: a consensus report. Cureus. 2025;17:e98371.

6. Gumbs A, Diana M, Rawicz-Prusyński K, et al. AIONS consensus conference on definitions of artificial intelligence surgery, surgomics and robotics. Art Int Surg. 2026;6:98-113.

7. Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181:1065-70.

8. Chung P, Swaminathan A, Goodell AJ, et al. Verifying facts in patient care documents generated by large language models using electronic health records. NEJM AI. 2026;3.

9. Lewis P, Perez E, Piktus A, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv Neural Inf Process Syst. 2020;33:9459-74. Available from: https://proceedings.neurips.cc/paper/2020/hash/6b493230-Abstract.html [Last accessed on 2 September 2026].

10. Alu FF, Oluwadare S. An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance. Front Artif Intell. 2026;9:1737532.

11. Lin C, Lin JY, Lin YS. Evidence-graded decision authorization for safe clinical AI: a constrained reasoning framework. medRxiv 2026; Epub ahead of print.

12. Bedi S, Cui H, Fuentes M, et al. Holistic evaluation of large language models for medical tasks with MedHELM. Nat Med. 2026;32:943-51.

13. World Wide Web Consortium. ODRL Information Model 2.2. W3C Recommendation, 15 February 2018. Available from: https://www.w3.org/TR/odrl-model/. [Last accessed on 2 September 2026].

14. Lawson J, Cabili MN, Kerry G, et al. The Data Use Ontology to streamline responsible access to human biomedical datasets. Cell Genom. 2021;1:100028.

15. Mozannar H, Sontag D. Consistent estimators for learning to defer to an expert. Proc Mach Learn Res. 2020;119:7076-87. Available from: https://proceedings.mlr.press/v119/mozannar20b.html [Last accessed on 2 September 2026].

16. Kompa B, Snoek J, Beam AL. Second opinion needed: communicating uncertainty in medical machine learning. NPJ Digit Med. 2021;4:4.

17. Machcha S, Yerra S, Gupta S, et al. Knowing when to abstain: medical LLMs under clinical uncertainty. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers); 2026 Mar; Rabat, Morocco. Stroudsburg, PA, USA: Association for Computational Linguistics, 2026; pp. 6153-82.

18. Mastari FA. The refusal stack - engineering medical AI for adversarial audit. Zenodo, 17 May 2026. Available from: https://zenodo.org/records/20257894 [Last accessed on 2 September 2026].

19. Li S, Balachandran V, Feng S, et al. MediQ: question-asking LLMs and a benchmark for reliable interactive clinical reasoning. Advances in Neural Information Processing Systems 37; 2024 Dec 10-15; Vancouver, BC, Canada. San Diego, California, USA: Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2024; pp. 28858-88.

20. Swaminathan A, Lopez I, Wang W, et al. Selective prediction for extracting unstructured clinical data. J Am Med Inform Assoc. 2024;31:188-97.

21. Watanabe Y, Kobashi Y, Kojima T, Iwasawa Y, Okuno Y, Matsuo Y. ClinDet-Bench: beyond abstention, evaluating judgment determinability of LLMs in clinical decision-making. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track); 2026 Jul; San Diego, California, USA. Stroudsburg, PA, USA: Association for Computational Linguistics, 2026; pp. 681-703.

22. Schneier B, Kelsey J. Cryptographic support for secure logs on untrusted machines. Proc 7th USENIX Security Symposium, San Antonio, TX, 26-29 January 1998; pp. 53-62. Available from: https://www.usenix.org/conference/7th-usenix-security-symposium/cryptographic-support-secure-logs-untrusted-machines. [Last accessed on 2 September 2026].

23. Kent K, Souppaya M. Guide to Computer Security Log Management. NIST Special Publication 800-92. Gaithersburg, MD: National Institute of Standards and Technology; 2006.

24. International Organization for Standardization. ISO 27789:2021. Health informatics - Audit trails for electronic health records. ISO, 2021. Available from: https://www.iso.org/standard/75313.html. [Last accessed on 2 September 2026].

25. Tith D, Lee JS, Suzuki H, et al. Application of blockchain to maintaining patient records in electronic health record for enhanced privacy, scalability, and availability. Healthc Inform Res. 2020;26:3-12.

26. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). EUR-Lex, 2024. Available from: https://eur-lex.europa.eu/eli/reg/2024/1689/oj. [Last accessed on 2 September 2026].

27. Medical Device Coordination Group. MDCG 2025-6: FAQ on Interplay between the Medical Devices Regulation (MDR) & In Vitro Diagnostic Medical Devices Regulation (IVDR) and the Artificial Intelligence Act (AIA). European Commission, June 2025. Available from: https://health.ec.europa.eu/latest-updates/mdcg-2025-6-faq-interplay-between-medical-devices-regulation-vitro-diagnostic-medical-devices-2025-06-19_en. [Last accessed on 2 September 2026].

28. Mascagni P, Vardazaryan A, Alapatt D, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg. 2022;275:955-61.

29. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation). OJ L 119, 4 May 2016; pp. 1-88. Available from: https://eur-lex.europa.eu/eli/reg/2016/679/oj. [Last accessed on 2 September 2026].