Inteligencia artificial en salud: de los algoritmos de diagnóstico a los modelos fundacionales. Aplicaciones clínicas, impacto y desafíos éticos

Olga Lucia Ostos Ortiz; Oscar Yecid Aparicio Gómez

doi:10.22490/26194759.10913

Vol. 9 No. 1 (2025): .

Published 2025-12-01

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Artículos

Artificial Intelligence in Healthcare: From Diagnostic Algorithms to Foundation Models. Clinical Applications, Impact, and Ethical Challenges

DOI: https://doi.org/10.22490/26194759.10913

Olga Lucia Ostos Ortiz Universidad Nacional Abierta y a Distancia

Oscar Yecid Aparicio Gómez Editic Research Center

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Abstract
References

Objective. To conduct a narrative and scoping review on the consolidated applications and current perspectives of artificial intelligence in global health systems during 2019–2025, with emphasis on authorized medical devices and emerging technologies—particularly large language models and multimodal systems—and their impact on precision medicine, clinical decision-making, translational research, and healthcare service delivery. Methods. A scoping review was conducted integrating systematic reviews, meta-analyses, intervention studies, and pragmatic trials retrieved from PubMed, Scopus, and Web of Science, as well as documents from the World Health Organization and the Food and Drug Administration. The analysis contrasted authorized medical devices with emerging literature on foundation models, prioritizing the assessment of methodological robustness. Results. Artificial intelligence has consolidated as a cross-cutting technology within health systems. In 2023, more than 23,000 articles on AI applications in health were published. By 2025, the FDA had authorized between 900 and 1,200 AI-enabled medical devices, predominantly in radiology, cardiology, and oncology, with growing implementations in primary care, nursing, pharmacotherapy, and psychiatry. Most deployments remain restricted to high-income settings with highly structured data environments, revealing priority opportunities in primary care, nursing, and mental health. Conclusions. Artificial intelligence has moved beyond proof-of-concept stages to become a pillar of health innovation in imaging and clinical decision-support systems. Its large-scale deployment requires advances in ethical governance, adapted regulatory frameworks, and clinical algorithmic literacy. The future depends on integrating technical excellence with health justice, population equity, and the preservation of professional autonomy in alignment with WHO guidelines.

keywords: artificial intelligence, deep learning; clinical, clinical decision support systems, primary care, mental health, large language models

1. WHO Guidance on Ethics and Governance of Artificial Intelligence for Health [Internet]. AI Ethics and Governance Institute; 2021 [citado 28 nov 2025]. Disponible en: https://www.who.int/publications/i/item/9789240029200

2. FDA Expands List of AI/ML-Enabled Medical Devices, Majority Focused on Radiology [Internet]. HealthHQ; 2025 [citado 28 nov 2025].

3. Omiye JA, Gui H, Rezaei SJ, Zou J, Daneshjou R. Large language models in medicine: the potentials and pitfalls. Ann Intern Med. 2024;177(2):210 220.

4. WHO – Ethics and Governance of Artificial Intelligence for Health: WHO Guidance [Internet]. 2021 [citado 28 nov 2025].

5. FDA – 950 AI/ML-enabled medical devices [Internet]. 2025 [citado 28 nov 2025].

6. Large Language Models in Medical Diagnostics: Scoping Review. J Med Internet Res. 2025;27:e72062.

7. WHO guidance on Artificial Intelligence to improve healthcare, mitigate risks worldwide. UN News [Internet]. 2021 [citado 28 nov 2025].

8. AI in medtech is booming. Track new devices here. MedTech Dive [Internet]. 2025 [citado 28 nov 2025].

9. Omar M, et al. Large language models in medicine: A review of current clinical trials. BMJ Open o similar. 2024.

10. Shaw J, et al. Research ethics and artificial intelligence for global health. BMJ Glob Health. 2024;9:e013xxx.

11. FDA's AI Medical Device List: Stats, Trends & Regulation [Internet]. Intuition Labs; 2025 [citado 28 nov 2025].

12. Thirunavukarasu AJ, et al. Large language models in medicine. Nat Med. 2023;29:1930 1940.

13. Organización Mundial de la Salud. Ethics and governance of artificial intelligence for health [Internet]. 2021 [citado 28 nov 2025].

14. U.S. Food and Drug Administration. Artificial Intelligence-Enabled Medical Devices [Internet]. 2025 [citado 28 nov 2025]. Disponible en: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices

15. Omiye JA, et al. Large Language Models in Medicine: The Potentials and Pitfalls. Ann Intern Med. 2024;177(2):210 220.

16. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2021.

17. The Medical Futurist Institute. The Current State of Over 1250 FDA-Approved, AI-Based Medical Devices [Internet]. 2025 [citado 28 nov 2025].

18. Zou S, et al. Large Language Models in Healthcare: A Review. IEEE; 2023.

19. World Health Organization. Ethics and governance of artificial intelligence for health: large multimodal models. Geneva: WHO; 2025.

20. Axtria. FDA-Approved Artificial Intelligence and Machine Learning-Enabled Medical Devices: An Updated Landscape 1995 2023 [Internet]. 2024 [citado 28 nov 2025].

21. Fairhurst V. Peer Review of “Artificial Intelligence in Healthcare: 2023 Snapshot of Current Uses and Future Opportunities”. J Med Internet Res. 2024.

22. Aggarwal R, et al. Diagnostic accuracy of deep learning in medical imaging. NPJ Digit Med. 2021;4:65.

23. Borkar SR. AI-Based Clinical Decision Support in Multidisciplinary Medicine. Healthcare Bulletin. 2025.

24. Ruksakulpiwat S, et al. A Systematic Review of the Application of Artificial Intelligence in Nursing Care. J Multidiscip Healthc. 2024.

25. Sriraman H, et al. A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnosis. Cancer Manag Res. 2024.

26. Elhaddad M, et al. AI-Driven Clinical Decision Support Systems. J Transl Med. 2024.

27. Khosravi M, et al. Artificial Intelligence and Decision-Making in Healthcare: A Review of Reviews. Digit Health. 2024.

28. Zhou SK, et al. A review of deep learning in medical imaging. Med Image Anal. 2021;69:101985.

29. Tun HM, et al. Trust in Artificial Intelligence–Based Clinical Decision Support Systems: Systematic Review. J Med Internet Res. 2025.

30. Gazquez-Garcia J, et al. AI in the Health Sector: Systematic Review of Key Skills for Health Care Professionals. JMIR Med Educ. 2025.

31. Lamba R, et al. Advances in AI for Medical Imaging: A Review of Machine and Deep Learning Methods. Procedia Comput Sci. 2025.

32. Khude H, et al. AI-driven clinical decision support systems in pharmacotherapy. Comput Methods Programs Biomed. 2025.

33. Alzghoul B, et al. Impact of Artificial Intelligence on Healthcare Quality. Open Public Health J. 2024.

34. Deep Learning for Medical Image Analysis: A Review. J Healthc Eng. 2023.

35. Cabello CAG, et al. Artificial-intelligence-based clinical decision support systems in primary care: a scoping review. (Revista de AP). 2024.

36. Ali O, et al. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl. 2023.

37. Deep Learning for Medical Imaging: A Comprehensive Review. Asian J Res Comput Sci. 2025.

38. Oei SP, et al. Artificial intelligence in clinical decision support and prediction of adverse events. Front Digit Health. 2025.

39. Ali O, et al. A systematic literature review of artificial intelligence in the healthcare sector: PDF version [Internet]. 2023.

40. Saratkar S, et al. Review of Machine Learning and Deep Learning for Dynamic Medical Imaging. IEEE; 2024.

41. Katonai G, et al. AI and Primary Care: Scoping Review. J Med Internet Res. 2025;27:e65950.

42. Cruz-Gonzalez P, et al. Artificial intelligence in mental health care: a systematic review. Psychol Med. 2025.

43. Ruksakulpiwat S, et al. Application of Artificial Intelligence in Nursing Care: Systematic Review. J Multidiscip Healthc. 2024.

44. Kueper JK, et al. Artificial Intelligence and Primary Care Research: Scoping Review. Ann Fam Med. 2020;18:250 258.

45. Poudel U, et al. AI in Mental Health: A Review of Technological Applications. Issues Ment Health Nurs. 2025.

46. Castrillón-Isaza KA, et al. Risks and Opportunities of Artificial Intelligence in Nursing: Ethical Scoping Review. Invest Educ Enferm. 2025.

47. Katonai G, et al. AI and Primary Care: Scoping Review (PDF). J Med Internet Res. 2025.

48. Sun J, et al. Practical AI application in psychiatry: historical review and future perspectives. Mol Psychiatry. 2025.

49. Koo TH. Systematic Review of the Application of Artificial Intelligence in Healthcare with a Focus on Nursing. Malays J Med Sci. 2024.

50. Katonai G, et al. AI and Primary Care: PubMed record. J Med Internet Res. 2025.

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How to Cite

Ostos Ortiz, O. L., & Aparicio Gómez, O. Y. (2025). Artificial Intelligence in Healthcare: From Diagnostic Algorithms to Foundation Models. Clinical Applications, Impact, and Ethical Challenges. Biociencias, 9(1), 95-103. https://doi.org/10.22490/26194759.10913

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