Proceedings of International Conference on Applied Innovation in IT  ·  2025/12/22  ·  Vol. 13  ·  Issue 5  ·  pp. 629–635
AI-Driven Medical Education for Personalized Health
Maria Zheleva, Boyan Jekov, Tamara Riskova and Mariana Filipova
The present study explores the integration of AI-driven solutions in the education of medical professionals and their role in achieving high-quality healthcare and personalized strategies. A range of AI tools is examined to evaluate how they shape training environments, support medical students during their studies, and influence their later professional practice. The primary objective is to analyze how AI contributes to the advancement of medical education and the transfer of acquired knowledge into daily clinical routines. While AI enables rapid access to information and decision support, medical professionals must also maintain the ability to make accurate judgments under pressure. This raises concerns about whether reliance on AI during training may reduce students’ capacity for independent and rapid clinical reasoning. The study further investigates the extent to which AI-based simulations can provide realistic and comprehensive training experiences. Beyond replicating surgical procedures, it is critical to assess whether such systems can recreate the psychological, emotional, and tactile aspects of practice—such as stress management, operating under pressure, and the physical sensations of interacting with human tissue. Special emphasis is placed on identifying the benefits for patients, particularly whether AI-enhanced education ultimately contributes to more effective, personalized healthcare outcomes.
Artificial Intelligence Medical Education Empathy Development Data Analysis Healthcare Technology.
References
  1. D. Castillejos, J. Noguez, J. Neri, A. Magana, and B. Benes, “A review of simulators with haptic devices for medical training,” J. Med. Syst., 2016, doi: 10.1007/s10916-016-0459-8.
  2. P. Dev, W. Heinrichs, P. Youngblood, S. Kung, R. Cheng, L. Kusumoto, and A. Hendrick, “Virtual patient model for multi-person virtual medical environments,” AMIA Annu. Symp. Proc., 2007.
  3. T. Rudroff, “Artificial intelligence as a replacement for animal experiments in neurology,” Neurology International, vol. 16, no. 4, 2024, doi: 10.3390/neurolint16040060.
  4. M. Glicksman et al., “AI and pain medicine education: Benefits and pitfalls,” Pain Practice, vol. 25, no. 1, 2024, doi: 10.1111/papr.13428.
  5. J. Hatherley, “Data over dialogue: Why artificial intelligence is unlikely to humanise medicine,” Computers and Society, 2025, doi: 10.26180/24955371.v1.
  6. R. Athira et al., “Alternatives to animal testing: Concepts, state of art, and regulations,” in Biomedical Product and Materials Evaluation, 2022, doi: 10.1016/B978-0-12-823966-7.00001-3.
  7. Y. Hicke et al., “MedSimAI: Simulation and formative feedback generation to enhance deliberate practice,” arXiv, 2025, doi: 10.48550/arXiv.2503.05793.
  8. S. Sarfaraz, Z. Sultan, and M. Zafar, “Use of artificial intelligence in medical education: A strength or an infirmity,” J. Taibah Univ. Med. Sci., 2023, doi: 10.1016/j.jtumed.2023.06.008.
  9. N. Rabbani, G. Kim, C. Suarez, and J. Chen, “Applications of machine learning in routine laboratory medicine: Current state and future directions,” Clinical Biochemistry, vol. 103, 2022, doi: 10.1016/j.clinbiochem.2022.02.011.
  10. C. Tsang, J. Cao, K. Sugand, J. Chiu, and F. Pretorius, “Face, content, construct validity and training effect of Touch Surgery™ as a surgical decision-making trainer for novices in open appendicectomy,” Int. J. Surg. Protocols, 2020, doi: 10.1016/j.isjp.2020.05.002.
  11. S. Dawe, J. Windsor et al., “A systematic review of surgical skills transfer after simulation-based training,” Annals of Surgery, 2014, doi: 10.1097/SLA.0000000000000245.
  12. J. Peters, G. Fries, L. Swanstrom, N. Soper, L. Sillin, B. Schirmer, and K. Hoffman, “Development and validation of a comprehensive program of education and assessment of the basic fundamentals of laparoscopic surgery,” Surgery, 2004, doi: 10.1016/s0039-6060(03)00156-9.
  13. S. Narayanan, R. Ramakrishna, E. Durairaj, and A. Das, “Artificial intelligence revolutionizing the field of medical education,” Cureus, 2023, doi: 10.7759/cureus.49604.
  14. R. D. Acton, “The evolving role of simulation in teaching surgery in undergraduate medical education,” Surg. Clin. North Am., vol. 95, no. 4, 2015, doi: 10.1016/j.suc.2015.04.001.
  15. I. Muhammad et al., “Artificial intelligence: Revolutionizing robotic surgery: Review,” Annals of Medicine and Surgery, 2024, doi: 10.1097/MS9.0000000000002426.
  16. N. Pakkasjarvi, T. Luthra, and S. Anand, “Artificial intelligence in surgical learning,” Surgeries, 2023, doi: 10.3390/surgeries4010010.
  17. A. Fazlollahi, R. Yilmaz, and A. Schwartz, “AI in surgical curriculum design and unintended outcomes for technical competencies in simulation training,” JAMA Network Open, 2023, doi: 10.1001/jamanetworkopen.2023.34658.
  18. L. Maab et al., “Artificial intelligence and ChatGPT in medical education: A cross-sectional questionnaire on students’ competence,” Journal of CME, 2024, doi: 10.1080/28338073.2024.2437293.
  19. F. Busch et al., “Global cross-sectional student survey on AI in medical, dental, and veterinary education,” BMC Medical Education, 2024, doi: 10.1186/s12909-024-06035-4.
  20. C. Spector, “Education scholar uses AI to help medical students hone diagnostic skills,” Stanford Report, 2024.
  21. Q. Li and Y. Qin, “AI in medical education: Student perception, curriculum recommendations and design suggestions,” BMC Medical Education, 2023, doi: 10.1186/s12909-023-04700-8.
  22. S. Alkhaaldi et al., “Medical student experiences and perceptions of ChatGPT and AI,” JMIR, 2023, doi: 10.2196/51302.
  23. A. Franco et al., “Diagnostic accuracy in family medicine residents using a clinical decision support system (DXplain): A randomized controlled trial,” Diagnosis, 2018, doi: 10.1515/dx-2017-0045.
  24. T. Tu et al., “Towards conversational diagnostic artificial intelligence,” Nature, 2025, doi: 10.1038/s41586-025-08866-7.
  25. M. Civaner, Y. Uncu, F. Bulut, E. Chalil, and A. Tatli, “Artificial intelligence in medical education: A cross-sectional needs assessment,” BMC Medical Education, 2022.
  26. W. Othman et al., “Tactile sensing for minimally invasive surgery: Conventional methods and emerging technologies,” Frontiers in Robotics and AI, 2022, doi: 10.3389/frobt.2021.705662.
  27. C. Elendu et al., “The impact of simulation-based training in medical education: A review,” Medicine, doi: 10.1097/MD.0000000000038813.
  28. H. Lin et al., “Can virtual reality be used for empathy education in medical students: A randomized case-control study,” BMC Medical Education, 2024, doi: 10.1186/s12909-024-06009-6.
  29. E. Dyer, “Using virtual reality in medical education to teach empathy,” J. Med. Libr. Assoc., vol. 106, no. 4, 2018, doi: 10.5195/jmla.2018.518.
  30. T. Mendolia, “Empathetic chatbot: Enhancing medical education with AI,” in Proc. 9th Int. Conf. Immersive Learning Research Network, 2023, doi: 10.56198/ITIG2U41K.
  31. P. Ab-Duhaa et al., “AI in personalized medicine: AI-generated therapy regimens based on genetic and medical history,” Annals of Medicine and Surgery, 2023, doi: 10.1097/MS9.0000000000001320.
  32. J. Pfeiffer, C. Meyer, C. Schulz et al., “FAMULUS: Interactive annotation and feedback generation for teaching diagnostic reasoning,” arXiv, 2019, doi: 10.48550/arXiv.1908.11254.
  33. P. Jackson et al., “Artificial intelligence in medical education: Perception among medical students,” BMC Medical Education, 2024, doi: 10.1186/s12909-024-05760-0.

Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0  ·  This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

ICAIIT 2026
International Conference on Applied Innovation in IT
Navigation
Publisher
ISSN2199-8876
Location Anhalt University of Applied Sciences
Phone +49 (0) 3496 67 5611
Address Building 01, Room 425
Bernburger Str. 55
D-06366 Köthen, Germany
Open Access License

All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.

Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.

© 2026 ICAIIT — International Conference on Applied Innovations in IT. Anhalt University of Applied Sciences, Köthen, Germany.
Visitors: site traffic counter