Discussion on the Regulatory Test of Artificial Intelligence-Enabled Medical Devices and Their Technical Potential in Tumor Immunity-Scilight

Trends in Immunotherapy

Review

Discussion on the Regulatory Test of Artificial Intelligence-Enabled Medical Devices and Their Technical Potential in Tumor Immunity

Downloads

Le Han, Yuzhe Chen, Lifeng Wang, & Xin Zong. (2025). Discussion on the Regulatory Test of Artificial Intelligence-Enabled Medical Devices and Their Technical Potential in Tumor Immunity. Trends in Immunotherapy, 9(3), 1–14. https://doi.org/10.54963/ti.v9i3.1217

Authors

  • Le Han

    School of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China
  • Yuzhe Chen

    Beijing Fengrui Pharmaceutical Technology Co., Ltd., Beijing 100176, China
  • Lifeng Wang

    Beijing Fengrui Pharmaceutical Technology Co., Ltd., Beijing 100176, China
  • Xin Zong

    School of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China

Received: 7 May 2025; Revised: 9 June 2025; Accepted: 23 June 2025; Published: 15 July 2025

The deep integration of AI and immunotherapy is reshaping the paradigm of cancer diagnosis and treatment. From biomarker discovery to personalised treatment, from adverse reaction warnings to empowering grassroots communities, despite bottlenecks such as data silos, algorithm transparency, and ethical controversies, the technical potential of AI has already begun to emerge. This paper examines the evolution of global AI medical device policies and product release trends over the past decade, identifying the issues and challenges posed by the current regulatory landscape, including: first, the structural imbalance between the regulatory system and the rate of technological innovation; second, the double-standardisation dilemma between risk classification and clinical validation; and third, the ethical paradox of data governance and algorithmic transparency. The challenges faced include: first, Technology Fusion: AI at the Crossroads with Synthetic Biology and Nanotechnology. Second, Algorithm Transparency and Ethical Paradox. Third, In-Depth Application of Regulatory Technology. Fourth, Collaborative Innovation in Industrial Ecology. Based on this, this paper provides systematic recommendations for addressing the regulation of AI medical devices: first, Building a Dynamic Adaptive Technology Supervision System. Second, Perfecting the Full Life Cycle Clinical Evidence Chain. Third, Create an Open and Collaborative Industrial Innovation Ecosystem. Fourth, Deepen International Regulatory Coordination and Cooperation. Recommendations for the regulation of AI medical devices in the field of immunotherapy: First, Multi-Modality Imaging and Treatment Integrated Platform. Second, Intelligent Empowerment of Primary Care. Third, Global Collaboration and Data Sharing.

Keywords:

AI Medical Devices; Regulatory Challenges; International Standards; Immunotherapy

References

  1. Software as a Medical Device (SaMD): Key Definitions. Available online: https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd (accessed on 3 May 2025).
  2. MDCG 2019-11: Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 – MDR and Regulation (EU) 2017/746 – IVDR. Available online: https://health.ec.europa.eu/document/download/b45335c5-1679-4c71-a91c-fc7a4d37f12b_en (accessed on 3 May 2025).
  3. Content of premarket submissions for device software functions - draft guidance for industry and food and drug administration staff. Available online: https://regulations.justia.com/regulations/fedreg/2023/06/14/2023-12723.html (accessed on 3 May 2025).
  4. COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE EUROPEAN COUNCIL AND THE COUNCIL. Available online: https://commission.europa.eu/document/download/926b3cb2-f027-40b6-ac7b-2c198a164c94_en?filename=COM_2024_146_1_EN.pdf (accessed on 3 May 2025).
  5. Ethics and governance of artificial intelligence for health. Available online: https://www.who.int/publications/i/item/9789240029200 (accessed on 3 May 2025).
  6. Samoili, S.; López Cobo, M.; Gómez, E.; et al. Defining Artificial Intelligence: Towards an Operational Definition and Taxonomy of Artificial Intelligence, EUR 30117 EN; Publications Office of the European Union: Luxembourg, 2020; pp. 15–20.
  7. Samoili, S.; López Cobo, M.; Delipetrev, B.; et al. Defining Artificial Intelligence 2.0: Towards an Operational Definition and Taxonomy for the AI Landscape, EUR 30873 EN; Publications Office of the European Union: Luxembourg, 2021; pp. 22–28.
  8. High-Level Expert Group on Artificial Intelligence. A definition of AI: main capabilities and scientific disciplines. EUR Expert Rep. 2018, 5, 1–18.
  9. Bragazzi, N.L.; Garbarino, S. Toward Clinical Generative AI: Conceptual Framework. JMIR AI 2024, 3, e55957.
  10. Drummond, D.; Adejumo, I.; Hansen, K.; Poberezhets, V.; Slabaugh, G.; Hui, C.Y. Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges. Breathe 2024, 20, 230189.
  11. Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts. Available online: https://artificialintelligenceact.eu/wp-content/uploads/2024/01/AIA-Final-Draft-21-January-2024.pdf (accessed on 3 May 2025).
  12. Artificial intelligence in healthcare. Applications, risks, and ethical and societal impacts. Available online: https://www.europarl.europa.eu/stoa/en/document/EPRS_STU(2022)729512 (accessed on 3 May 2025).
  13. Schmitt, L. Mapping global AI governance: a nascent regime in a fragmented landscape. AI Ethics 2022, 2, 303–314.
  14. Salthouse, T.A. Localizing age-related individual differences in a hierarchical structure. Intelligence 2004, 32, 503–521.
  15. GMDN Working Group. Annual Report on AI Device Classification Trends. In Proceedings of the Global Medical Device Nomenclature Annual Conference, London, UK, 15–17 May 2024.
  16. Consolidated Version of MDR (2023/C 247/01). Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=oj:JOC_2022_247_R_TOC (accessed on 3 May 2025).
  17. Deary, I.J.; Penke, L.; Johnson, W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 2010, 11, 201–211.
  18. White Paper on the Regulation of Artificial Intelligence Medical Devices. Available online: https://www.mhc.ie/uploads/MHC1510_AI_Medical_Devices_%28White_Paper%29.pdf (accessed on 3 May 2025).
  19. Software as a medical device: clinical evaluation. Available online: https://www.imdrf.org/working-groups/software-medical-device-samd (accessed on 3 May 2025).
  20. Ethics guidelines trustworthy AI. Available online: https://www.europarl.europa.eu/cmsdata/196377/AI%20HLEG_Ethics%20Guidelines%20for%20Trustworthy%20AI.pdf (accessed on 3 May 2025).
  21. Information technology — vocabulary. Available online: https://www.iso.org/committee/6794475.html (accessed on 3 May 2025).
  22. “Software as a Medical Device”: possible framework for risk categorization and corresponding considerations. Available online: https://www.imdrf.org/working-groups/software-medical-device-samd (accessed on 3 May 2025).
  23. Technical guideline on AI-aided software. Available online: https://english.mofcom.gov.cn/Policies/index.html (accessed on 3 May 2025).
  24. Government response to consultation on the future regulation of medical devices in the United Kingdom. Available online: https://assets.publishing.service.gov.uk/media/62b577f6d3bf7f0b00165a32/Government_response_to_consultation_on_the_future_regulation_of_medical_devices_in_the_United_Kingdom.pdf (accessed on 3 May 2025).
  25. Elgarba, B.M.; Fontenele, R.C.; Tarce, M.; Jacobs, R. Artificial intelligence serving pre-surgical digital implant planning: A scoping review. J. Dent. 2024, 143, 104862.
  26. Alderman, J.E.; Palmer, J.; Laws, E. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digit. Health 2025, 7, e64–e88.
  27. Peng, L.; Liu, X.Y. Analysis on product registration of Class Ⅲ deep learning SaMD. Chin. Digit. Med. 2023, 18, 32–36.
  28. Yu, J.; Zhang, J.; Sengoku, S. Innovation Process and Industrial System of US Food and Drug Administration–Approved Software as a Medical Device: Review and Content Analysis. J. Med. Internet Res. 2023, 25, e47505.
  29. Liu, X.; Gao, K.; Liu, B.; et al. Advances in deep learning-based medical image analysis. Health Data Sci. 2021, 1, 87–86793.
  30. Onodera, R.; Sengoku, S. Innovation process of mHealth: an overview of FDA-approved mobile medical applications. Int. J. Med. Inform. 2018, 118, 65–71.
  31. Hiroshi, F.; Takayuki, I.; Shigehiko, K.; et al. Handbook of Practical Image Analysis in Medicine; Ohmsha, Ltd: Tokyo, Japan, 2012; pp. 189–210.
  32. Nishitani, H. Clinical significance and problems of three dimensional CT data: clinical practice overwhelmed by huge 3DCT data. Med. Imaging Technol. 2007, 25, 75.
  33. Q3 2022 digital health funding: the market isn’t the same as it was. Available online: https://rockhealth.com/insights/q3-2022-digital-health-funding-the-market-isnt-the-same-as-it-was/ (accessed on 3 May 2025).
  34. Siemens Healthineers completes acquisition of Varian, strengthening its position as a holistic partner in healthcare; In an up-to $200M acquisition by Nanox, Zebra Medical Vision brings its AI to reimagine radiology globally. Available online: https://www.siemens-healthineers.com/press/releases/varian-closing (accessed on 3 May 2025).
  35. Sreenivasan, M.; Anu Mary Chacko. Data Analytics in Biomedical Engineering and Healthcare; Academic Press: Cambridge, MA, USA, 2021; pp. 35–58.
  36. Fu, L.X.; Cui, Y.M. The applications and advances of artificial intelligence in drug regulation: A global perspective. Drug Regul. Perspect. 2024, 36, 45–58.
  37. Guangdong Provincial Department of Industry and Information Technology. Implementation Plan for the Digital and Intelligent Transformation of the Pharmaceutical Industry (2025–2030). Available online: http://www.gdei.gov.cn/zwgk/zcwj/wjfb/gh/art/2025/art_12345.html (accessed on 2 May 2025).
  38. REPORT on the proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union Legislative Acts. Available online: https://www.europarl.europa.eu/doceo/document/A-9-2023-0188_EN.html (accessed on 3 May 2025).
  39. WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use. Available online: https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use (accessed on 3 May 2025).
  40. O’Meara, S. China’s data-driven dream to overhaul health care. Nature 2021, 592, 31–33.
  41. Smith, J.; Lee, A. Regulatory approaches towards AI Medical Devices: A Comparative Study of the United States, the European Union and China. Health Policy 2025, 130, 289–302.
  42. Liu, Y.H.; Dillon, T. Regulatory responses and approval status of artificial intelligence medical devices with a focus on China. NPJ Digit. Med. 2024, 7, 255.
  43. Sai, S.; Gaur, A.; Sai, R.; Chamola, V.; Guizani, M.; Rodrigues, J.J.P.C. Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations. IEEE Access 2024, 12, 31078–31106.
  44. Caiado, F.; Ukolov, A. The history, current state and future possibilities of the non-invasive brain computer interfaces. Med. Novel Technol. Devices 2025, 25, 100353.
  45. Musamih, A.; Yaqoob, I.; Salah, K.; Jayaraman, R.; Al-Hammadi, Y.; Omar, M. Metaverse in Healthcare: Applications, Challenges, and Future Directions. IEEE Consum. Electron. Mag. 2023, 12, 33–46.
  46. Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019, 25, 44–56.