Italian Journal of Health Education, Sports and Inclusive Didactics
CLINICAL VISION: NEW SCENARIOS BETWEEN ARTIFICIAL INTELLIGENCE AND THE NEED FOR AN "EMBODIED FIRST" CLINICAL APPROACH
Abstract
This work examines the integration of artificial intelligence (AI) in the healthcare field, highlighting the need for an "embodied first" clinical approach. In this sense, a hybrid human-machine model is proposed that leverages the computational power of AI while preserving the irreplaceable aspects of human judgment. Consistent with the aforementioned, the English healthcare model is analyzed, exploring the benefits derived from this integration, which can be attributed to greater efficiency while preserving privacy. Furthermore, the new frontiers of clinical training are being explored, emphasizing the need to prepare future doctors to collaborate with AI systems, prioritizing critical thinking and the ability to manage any potential hallucinations of these systems. It concludes by highlighting how the future of healthcare lies in the development of integrated approaches that combine technological and human capabilities to foster a post-pharmaceutical revolution.
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Afzal, S., Dhamecha, T. I., Gagnon, P., Nayak, A., Shah, A., Carlstedt-Duke, J., Pathak, S., Mondal, S., Gugnani, A., Zary, N., & Chetlur, M. (2020). AI Medical School Tutor: Modelling and Implementation. In M. Michalowski & R. Moskovitch (A c. Di), Artificial Intelligence in Medicine (pp. 133–145). Springer International Publishing. https://doi.org/10.1007/978-3-030-59137-3_13
Ahmad, S. (2024). Need for a Holistic Approach to Health Care. Shanlax International Journal of Arts, Science and Humanities, 11(S1-June), Articolo S1-June. https://doi.org/10.34293/sijash.v11iS1-June.7786
Ahmadi, Dr. R. (2024). Emerging Innovations in AI-Driven Medical Imaging: Elevating Diagnostic Precision and Therapeutic Decision-Making. Journal of AI-Powered Medical Innovations (International online ISSN: 3078-1930), 1(1), 1–28. https://doi.org/10.60087/japmi.vol01.issue01.p28
Ali, R., & Cui, H. (2024). Unleashing the potential of AI in modern healthcare: Machine learning algorithms and intelligent medical robots. Research on Intelligent Manufacturing and Assembly, 3(1), 100–108. https://doi.org/10.25082/RIMA.2024.01.002
Andriole, S. J. (2023). IT Empowering Rescuers and First Responders in Saving Lives. IT Professional, 25(6), 26–28. IT Professional. https://doi.org/10.1109/MITP.2023.3345592
Archana, K. (2024). Responsible AI Implementation in Healthcare Organizations: In B. O. Soufiene & C. Chakraborty (A c. Di), Advances in Healthcare Information Systems and Administration (pp. 1–14). IGI Global. https://doi.org/10.4018/979-8-3693-6294-5.ch001
Bhagat, M. I. A., Wankhede, M. K. G., Kopawar, M. N. A., & Sananse, P. D. A. (2024). Artificial Intelligence in Healthcare: A Review. International Journal of Scientific Research in Science, Engineering and Technology, 11(4), Articolo 4. https://doi.org/10.32628/IJSRSET24114107
Bhattacharya, S., & Makin, M. (2023). Learning from hospital deaths. Medico-Legal Journal, 91(1), 39–41. https://doi.org/10.1177/00258172221113982
Bortolini, V. S., Engelmann, W., & Garcia, A. D. S. (2024). Artificial Intelligence in Medicine: A systematic literature review of emerging risks and challenges. Brazilian Journal of Law, Technology and Innovation, 2(2), 1–18. https://doi.org/10.59224/bjlti.v2i2.1-18
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1), 139–159. https://doi.org/10.1016/0004-3702(91)90053-M
Cheng, Q., & Dong, Y. (2022). Da Vinci Robot-Assisted Video Image Processing under Artificial Intelligence Vision Processing Technology. Computational and Mathematical Methods in Medicine, 2022, 1–10. https://doi.org/10.1155/2022/2752444
Couzin-Frankel, J. (2019). Medicine contends with how to use artificial intelligence. Science, 364(6446), 1119–1120. https://doi.org/10.1126/science.364.6446.1119
Cybenko, G. (1996). Neural networks in computational science and engineering. IEEE Computational Science and Engineering, 3(1), 36–42. https://doi.org/10.1109/99.486759
Domrös-Zoungrana, D., Rajaeean, N., Boie, S., Fröling, E., & Lenz, C. (2024). Medical Education: Considerations for a Successful Integration of Learning with and Learning about AI. Journal of Medical Education and Curricular Development, 11, 23821205241284719. https://doi.org/10.1177/23821205241284719
Francis, J., Varghese, J. V., & Thomas, A. (2023). Impact of artificial intelligence on healthcare. International Journal of Advances in Medicine, 10(10), 737–743. https://doi.org/10.18203/2349-3933.ijam20232839
Golpayegani, D., Hovsha, J., Rossmaier, L. W. S., Saniei, R., & Misic, J. (2022). Towards a Taxonomy of AI Risks in the Health Domain. 2022 Fourth International Conference on Transdisciplinary AI (TransAI), 1–8. https://doi.org/10.1109/TransAI54797.2022.00007
Gray, K., Slavotinek, J., Dimaguila, G. L., & Choo, D. (2022). Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs. JMIR Medical Education, 8(2), e35223. https://doi.org/10.2196/35223
Guerschberg, L. (2025). De la Automatización a la Autonomía: Consecuencias de la Inteligencia Artificial en la Cuarta Revolución Industrial: From Automation to Autonomy: The Consequences of Artificial Intelligence in the Fourth Industrial Revolution. Multidisciplinary Latin American Journal (MLAJ), 3(1), 22–42. https://doi.org/10.62131/MLAJ-V3-N1-002
Herzog, D. J., & Herzog, N. J. (2024). Towards a potential paradigm shift in health data collection and analysis: Contemporary challenges of Human-Machine interaction. Metaverse, 5(1), 2690. https://doi.org/10.54517/m.v5i1.2690
Hsiao, J. H. (2024). Understanding Human Cognition Through Computational Modeling. Topics in Cognitive Science, 16(3), 349–376. https://doi.org/10.1111/tops.12737
Iacobucci, G. (2019). UK hospital uses AI to predict which patients are most likely to miss appointments. BMJ, l1792. https://doi.org/10.1136/bmj.l1792
Joshi, A., Singh, R., & Rani, S. (2024). Strategic Adoption of Artificial Intelligence for Human Resource Management Practices Transforming Healthcare Sector. The International Journal of Education Management and Sociology, 3(3), Articolo 3. https://doi.org/10.58818/ijems.v3i3.133
Kangra, K., & Singh, J. (2024). Artificial Intelligence in Healthcare: A Paradigm Shift. In Artificial Intelligence Technology in Healthcare. CRC Press.
Klassner, F. (1996). Artificial intelligence: Introduction. XRDS: Crossroads, The ACM Magazine for Students, 3(1), 2. https://doi.org/10.1145/332148.332149
Kollars Jr, T. M. (2024). Unique technologies: Saving lives to save souls. International Journal of Family & Community Medicine, 8(1), 1–7. https://doi.org/10.15406/ijfcm.2024.08.00341
Kumar, D. (2024). AI-DRIVEN AUTOMATION IN ADMINISTRATIVE PROCESSES: ENHANCING EFFICIENCY AND ACCURACY. International Journal of Engineering Science and Humanities, 14(Special Issue 1), 256–265. https://doi.org/10.62904/qg004437
Kundu, S., Nayak, K., Kadavigere, R., Pendem, S., & . P. (2024). Evaluation of positioning accuracy, radiation dose and image quality: Artificial intelligence based automatic versus manual positioning for CT KUB. F1000Research, 13, 683. https://doi.org/10.12688/f1000research.150779.1
Lakoff, G., & Johnson, M. (1980). The Metaphorical Structure of the Human Conceptual System. Cognitive Science, 4(2), 195–208. https://doi.org/10.1207/s15516709cog0402_4
Langley, P. (2024). Symbols and search in humans and machines. In G. Gigerenzer, S. Mousavi, & R. Viale (A c. Di), Elgar Companion to Herbert Simon (pp. 33–54). Edward Elgar Publishing. https://doi.org/10.4337/9781800370685.00010
Lewis, J., & Holm, S. (2023). Towards a concept of embodied autonomy: In what ways can a patient’s body contribute to the autonomy of medical decisions? Medicine, Health Care and Philosophy, 26(3), 451–463. https://doi.org/10.1007/s11019-023-10159-7
Lopez-Lopez, V., Sanchez-Esquer, I., Crespo, M. J., Navarro, M. Á., Brusadin, R., Lopez-Conesa, A., Navarro, A., Miura, K., Gomez-Valles, P., Cayuela, V., & Robles-Campos, R. (2023). Development and Validation of Advanced Three-dimensional Navigation Device Integrated in da Vinci Xi® Surgical Robot for Hepatobiliary Surgery: Pilot Study. HPB, 25, S379–S380. https://doi.org/10.1016/j.hpb.2023.07.378
MacLennan, A. (1996). The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, edited by Luc Steels and Rodney Brooks. Journal of the American Society for Information Science. https://www.semanticscholar.org/paper/The-Artificial-Life-Route-to-Artificial-Building-by-MacLennan/928486554130f3a3450c8ad466e303ddb8e11d2e
Malerbi, F. K., Nakayama, L. F., Gayle Dychiao, R., Zago Ribeiro, L., Villanueva, C., Celi, L. A., & Regatieri, C. V. (2023). Digital Education for the Deployment of Artificial Intelligence in Health Care. Journal of Medical Internet Research, 25, e43333. https://doi.org/10.2196/43333
McCarthy, J., Minsky, ML, Rochester, N., e Shannon, CE (2006). Una proposta per il progetto di ricerca estivo di Dartmouth sull'intelligenza artificiale, 31 agosto 1955. AI Magazine , 27 (4), 12. https://doi.org/10.1609/aimag.v27i4.1904
Mensah, G. B. (2024). AI and Medical Negligence. Africa Journal For Regulatory Affairs (AJFRA), 2024(1), 46–61. https://doi.org/10.62839/AJFRA.v01i01.46-61
Miller, D. D., & Brown, E. W. (2018). Artificial Intelligence in Medical Practice: The Question to the Answer? The American Journal of Medicine, 131(2), 129–133. https://doi.org/10.1016/j.amjmed.2017.10.035
Mittal, A. (2023). A Technological View of Artificial Intelligence in US Healthcare. OA J Applied Sci Technol, 1(2), 142–144.
Morenikeji I Yisa. (2024). Leveraging Artificial Intelligence in healthcare to optimize patient outcomes, with specialized staff training programs. World Journal of Advanced Research and Reviews, 24(3), 3078–3093. https://doi.org/10.30574/wjarr.2024.24.3.4041
Muley, A., Muzumdar, P., Kurian, G., & Basyal, G. P. (2023). Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework. Asian Journal of Medicine and Health, 21(10), 276–291. https://doi.org/10.9734/ajmah/2023/v21i10903
Nabila, N., & Ayuningtyas, D. (2024). The Effectivity of Outpatient Waiting Time in Hospital through Online or Web-based Reservation (Literature Review). Asian Journal of Engineering, Social and Health, 3(8), 1725–1739. https://doi.org/10.46799/ajesh.v3i8.370
Narkhede, J. (2024). Artificial Intelligence in Drug Discovery and Drug Design. International Journal of Pharmaceutical Research and Applications, 09(05), 640–655. https://doi.org/10.35629/4494-0905640655
Nwagbara, U. I., Hlongwana, K. W., & Chima, S. C. (2024). Mapping evidence on the factors contributing to long waiting times and interventions to reduce waiting times within primary health care facilities in South Africa: A scoping review. PLOS ONE, 19(8), e0299253. https://doi.org/10.1371/journal.pone.0299253
Owen, R., Ashton, R. E. M., Ferraro, F. V., Skipper, L., Bewick, T., Leighton, P., Phillips, B. E., & Faghy, M. A. (2023). Forming a consensus opinion to inform long COVID support mechanisms and interventions: A modified Delphi approach. eClinicalMedicine, 62, 102145. https://doi.org/10.1016/j.eclinm.2023.102145
Pallavi Patil & Kirankumari Patil. (2023). A Review on Disease Prediction Using Artificial Intelligence. Journal Electrical and Computer Experiences, 1(1), 1–10. https://doi.org/10.59535/ece.v1i1.8
Pargaien, A. V., Pargaien, S., Nawaz, A., & Kumar, T. (2024). A Review on the Integration of Artificial Intelligence in Healthcare. 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), 880–884. https://doi.org/10.1109/ICESC60852.2024.10689737
Popov, V., Mateju, N., Jeske, C., & Lewis, K. O. (2024). Metaverse-based simulation: A scoping review of charting medical education over the last two decades in the lens of the ‘marvelous medical education machine’. Annals of Medicine, 56(1), 2424450. https://doi.org/10.1080/07853890.2024.2424450
Preda, A., Păcuraru, M., Tiriteu, Ștefan, Chirvase, C. S., Buhus, Ștefănuț, Zamfir, A., & Croitoru, E.-O. (2024). Toward Medical Services Quality Improvement through Industry 4.0 in Healthcare. MANAGEMENT AND ECONOMICS REVIEW, 9(2), 401–408. https://doi.org/10.24818/mer/2024.02-14
Rachman, N. O., Adhani, R., & Rahman, F. (2024). Factors That Affect Waiting Time Polyclinic Hospital: Literature Review. International Journal of Research and Review, 11(9), 234–243. https://doi.org/10.52403/ijrr.20240925
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0
Rakibul Hasan Chowdhury. (2024). Intelligent systems for healthcare diagnostics and treatment. World Journal of Advanced Research and Reviews, 23(1), 007–015. https://doi.org/10.30574/wjarr.2024.23.1.2015
Rana, M., Sall, S., Bijoor, V. S., Gaiwad, V., Gaikwad, U. V., Patil, P., & Meher, K. (2024). Obstacles to the Full Realization and Adoption of Artificial Intelligence (AI). South Eastern European Journal of Public Health, 1003–1016. https://doi.org/10.70135/seejph.vi.2251
Rathore, Y., Mishra Chaturvedi, V., Sujay Madhukar, K., Karwande, V. S., Rokade, A. H., & Nagargoje, Y. (2023). Patient Engagement and Satisfaction in Ai-Enhanced Healthcare Management. 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), 1–7. https://doi.org/10.1109/ICAIIHI57871.2023.10489712
Reddy, S., & Cooper, P. (2021). Health Workforce Learning in Response to Artificial Intelligence. In K. Butler-Henderson, K. Day, & K. Gray (A c. Di), The Health Information Workforce (pp. 129–137). Springer International Publishing. https://doi.org/10.1007/978-3-030-81850-0_8
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510
Rozillio-Mercado, E., López-Anza, D., Ortega-Ortuño, G. L., Lee Lee, S. H. J., Minian-Okon, J., Gutiérrez-Gurza, R. A., Basson-Amkie, M., Ramírez-Santamaría, A. L., Coutinho-Thomas, D. J., & Pérez-Bermúdez, B. (2024). Inteligencia Artificial en Medicina, usos Actuales y Futuras Perspectivas. Ciencia Latina Revista Científica Multidisciplinar, 7(6), 6286–6303. https://doi.org/10.37811/cl_rcm.v7i6.9167
Sachdeva, G., Han, D., Keane, P. A., Denniston, A. K., & Liu, X. (2023). Demonstrating Clinical Impact for AI Interventions: Importance of Robust Evaluation and Standardized Reporting. In M. F. Byrne, N. Parsa, A. T. Greenhill, D. Chahal, O. Ahmad, & U. Bagci (A c. Di), AI in Clinical Medicine (1a ed., pp. 459–468). Wiley. https://doi.org/10.1002/9781119790686.ch42
Selvakumar, P., Sharma, R., Karunakaran, N. B., S. R., M., Srivastava, P., & T. C., M. (2025). Health Services and AI Technologies: In A. Günar (A c. Di), Advances in Finance, Accounting, and Economics (pp. 375–394). IGI Global. https://doi.org/10.4018/979-8-3693-7036-0.ch016
Singh, P., Singh, A. K., Verma, N. K., Kumar, A., Chegini, Z., & Malviya, A. (2024). The Transformative Role of Artificial Intelligence in Pharmaceutical Healthcare: A Comprehensive Review. Scholars Academic Journal of Pharmacy, 13(05), 139–144. https://doi.org/10.36347/sajp.2024.v13i05.002
Sisk, B. A., Antes, A. L., Lin, S. C., Nong, P., & DuBois, J. M. (2024). Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare. Learning Health Systems, e10429. https://doi.org/10.1002/lrh2.10429
Soleas, E. K., Dittmer, D., Waddington, A., & Van Wylick, R. (2025). Demystifying Artificial Intelligence for Health Care Professionals: Continuing Professional Development as an Agent of Transformation Leading to Artificial Intelligence–Augmented Practice. Journal of Continuing Education in the Health Professions, 45(1), 52–55. https://doi.org/10.1097/CEH.0000000000000571
Song, B., Zhu, Q., & Luo, J. (2024). Human-AI collaboration by design. Proceedings of the Design Society, 4, 2247–2256. https://doi.org/10.1017/pds.2024.227
Srinivasa, K. G., Kurni, M., & Saritha, K. (2022). Embodied Learning. In K. G. Srinivasa, M. Kurni, & K. Saritha (A c. Di), Learning, Teaching, and Assessment Methods for Contemporary Learners: Pedagogy for the Digital Generation (pp. 177–200). Springer Nature. https://doi.org/10.1007/978-981-19-6734-4_8
Suazo Galdames, I. (2024). From Anatomy to Algorithm: Scope of AI-Assisted Diagnostic Competencies in Health Sciences Education. International Journal of Medical and Surgical Sciences, 1–24. https://doi.org/10.32457/ijmss.v11i3.2818
Tanton, T. (2023). Embodied Cognition: Literature, History, and Concepts. In T. Tanton (A c. Di), Corporeal Theology: The Nature of Theological Understanding in Light of Embodied Cognition (p. 0). Oxford University Press. https://doi.org/10.1093/oso/9780192884589.003.0003
Taylor, S. S. (2022). Reinforcement. In Reinforcement. Routledge. https://doi.org/10.4324/9780367198459-REPRW174-1
Uygun İLi̇Khan, S., Özer, M., Tanberkan, H., & Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Sciences, 54(3), 483–492. https://doi.org/10.55730/1300-0144.5814
Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.
Warwick, K., & Shah, H. (2016). Turing’s Imitation Game: Conversations with the Unknown (1a ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781107297234
Warwick, W., Allender, L., & Yen, J. (2009). Editors’ Introduction to the Special Issue on Developing and Understanding Computational Models of Macrocognition. Journal of Cognitive Engineering and Decision Making, 3(2), 93–96. https://doi.org/10.1518/155534309X441826
Williams, C., & Gibbs, P. (2024). Who is bearing the brunt of the increasing cost of cancer care? Medical Journal of Australia, 221(2), 92–93. https://doi.org/10.5694/mja2.52365
World Health Statistics 2023: Monitoring Health for the SDGs, Sustainable Development Goals (1st ed). (2023). World Health Organization.
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., … Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179
Yalçın, B. M. (2024). Current problems in family medicine and scientific research. The Journal of Turkish Family Physician, 15(2), 72–75. https://doi.org/10.15511/tjtfp.24.00272
Yang, X. (2024). The applications of artificial intelligence in personalized medicine. Applied and Computational Engineering, 71(1), 47–51. https://doi.org/10.54254/2755-2721/71/20241625
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