ARTIFICIAL INTELLIGENCE IN MODERN MEDICINE: APPLICATIONS, CHALLENGES, AND ETHICAL CONSIDERATIONS

Gabriela Krok; Anna  Kułach; Zofia  Wcisło; Karolina  Lach; Patrycja  Piekarska; Adrianna  Purwin; Grzegorz  Szmit; Maria  Chmielewska; Weronika  Basak; Katarzyna  Siwiec; Łukasz  Lamparski; Wiktoria  Modrzejewska

doi:10.31435/ijitss.1(49).2026.5242

Authors

Gabriela Krok Henryk Klimontowicz Hospital in Gorlice, Gorlice, Poland https://orcid.org/0009-0000-3969-9929
Anna Kułach Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0000-6476-8869
Zofia Wcisło Military Institute of Medicine, Warsaw, Poland https://orcid.org/0009-0009-9058-4882
Karolina Lach Medical University of Warsaw, Warsaw, Poland https://orcid.org/0000-0002-0529-1623
Patrycja Piekarska Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0007-0085-6617
Adrianna Purwin Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0008-5697-3721
Grzegorz Szmit Military Institute of Medicine, Warsaw, Poland https://orcid.org/0009-0000-5695-0388
Maria Chmielewska Mazovian Brodnowski Hospital, Warsaw, Poland https://orcid.org/0009-0002-7702-8282
Weronika Basak Military Institute of Medicine, Warsaw, Poland https://orcid.org/0009-0007-1788-3259
Katarzyna Siwiec Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0001-6411-9727
Łukasz Lamparski National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland https://orcid.org/0009-0005-3143-3583
Wiktoria Modrzejewska National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland https://orcid.org/0009-0006-9165-7404

DOI:

https://doi.org/10.31435/ijitss.1(49).2026.5242

Keywords:

AI in Medicine, Modern Medicine, AI-based Clinical Decision Support Systems, Healthcare Applications, Big Data in Medicine

Abstract

Artificial Intelligence (AI) has rapidly emerged as a transformative force in modern medicine, offering significant advancements across diagnostics, treatment planning, patient care, and public health management. Recent innovations in machine learning, natural language processing, robotics, and data-driven decision support have enhanced accuracy, efficiency, and personalization in clinical practice. AI applications span numerous specialties, including radiology, pathology, emergency medicine, aesthetic medicine, and public health, with demonstrated benefits such as improved imaging interpretation, automated histopathological analysis, optimized triage, predictive modeling for disease prevention, and patient-centered treatment planning. Despite its potential, integrating AI into healthcare presents technical, organizational, and ethical challenges, including data quality limitations, algorithmic bias, transparency and interpretability issues, cybersecurity vulnerabilities, and regulatory complexities. Ethical concerns involve patient privacy, fairness, and the distribution of responsibility for AI-guided clinical decisions. Looking forward, the responsible deployment of AI will require continuous model monitoring, integration with clinical workflows, equitable training datasets, and collaborative oversight to ensure that AI complements healthcare professionals, promotes safety, and maximizes benefits for both individual patients and populations.

References

Carini, C., & Seyhan, A. A. (2024). Tribulations and future opportunities for artificial intelligence in precision medicine. Journal of Translational Medicine, 22, 411. https://doi.org/10.1186/s12967-024-05067-0

Marques, M., Almeida, A., & Pereira, H. (2024). The medicine revolution through artificial intelligence: Ethical challenges of machine learning algorithms in decision-making. Cureus, 16(9). https://doi.org/10.7759/cureus.69405

Basubrin, O. (2025). Current status and future of artificial intelligence in medicine. Cureus, 17(1). https://doi.org/10.7759/cureus.77561

Gowda, U. (2025). Ethical AI in medicine: Balancing innovation with regulation and compliance. International Journal of Science and Technology, 2(1), 34. https://www.ijstjournal.com/papers/volume-2/issue-1/ijst241024/

Chustecki, M. (2024). Benefits and risks of AI in health care: Narrative review. Interactive Journal of Medical Research, 13. https://doi.org/10.2196/53616

Bhandari, A. (2024). Revolutionizing radiology with artificial intelligence. Cureus, 16(10). https://doi.org/10.7759/cureus.72646

Lesaunier, A., Khlaut, J., Dancette, C., Tselikas, L., Bonnet, B., & Boeken, T. (2025). Artificial intelligence in interventional radiology: Current concepts and future trends. Diagnostic and Interventional Imaging. https://doi.org/10.1016/j.diii.2024.08.004

Carriero, S., Cannella, R., Cicchetti, F., Angileri, A., Bruno, A., Biondetti, P., Colciago, R. R., D’Antonio, A., Della Pepa, G., Grassi, F., Granata, V., Lanza, C., Santicchia, S., Miceli, A., Piras, A., Salvestrini, V., Santo, G., Pesapane, F., Barile, A., Carrafiello, G., & Giovagnoni, A. (2025). AI revolution in radiology, radiation oncology and nuclear medicine: Transforming and innovating the radiological sciences. Journal of Medical Imaging and Radiation Oncology, 69(6), 649–659. https://doi.org/10.1111/1754-9485.13880

Lin, C., Tsai, D. J., Wang, C. C., Chao, Y. P., Huang, J. W., Lin, C. S., & Fang, W. H. (2024). Osteoporotic precise screening using chest radiography and artificial neural network: The OPSCAN randomized controlled trial. Radiology, 311(3). https://doi.org/10.1148/radiol.231937

Glielmo, P., Fusco, S., Gitto, S., Zantonelli, G., Albano, D., Messina, C., Sconfienza, L. M., & Mauri, G. (2024). Artificial intelligence in interventional radiology: State of the art. European Radiology Experimental, 8, 62. https://doi.org/10.1186/s41747-024-00452-2

Langlotz, C. P. (2023). The future of AI and informatics in radiology: 10 predictions. Radiology, 309(1). https://doi.org/10.1148/radiol.231114

Guo, L., Zhou, C., Xu, J., et al. (2024). Deep learning for chest X-ray diagnosis: Competition between radiologists with or without artificial intelligence assistance. Journal of Digital Imaging, 37, 922–934. https://doi.org/10.1007/s10278-024-00990-6

Ivanov, V., Khalid, U., Gurung, J., Dimov, R., Chonov, V., Uchikov, P., Kostov, G., & Ivanov, S. (2025). Use of AI histopathology in breast cancer diagnosis. Medicina, 61(10). https://doi.org/10.3390/medicina61101878

Cristian, M., Așchie, M., Deacu, M., Boșoteanu, M., Bălțătescu, G. I., Stoica, A. G., Nicolau, A. A., Poinăreanu, I., & Orășanu, C. I. (2023). Comparison of Ki67 proliferation index in gastrointestinal non-Hodgkin large B-cell lymphomas: The conventional method of evaluation or AI evaluation? Diagnostics, 13(17), 2775. https://doi.org/10.3390/diagnostics13172775

Shafi, S., & Parwani, A. V. (2023). Artificial intelligence in diagnostic pathology. Diagnostic Pathology, 18, 109. https://doi.org/10.1186/s13000-023-01375-z

Da’Costa, A., Teke, J., Origbo, J. E., Osonuga, A., Egbon, E., Olawade, D. B., et al. (2025). AI-driven triage in emergency departments: A review of benefits, challenges, and future directions. International Journal of Medical Informatics, 197. https://doi.org/10.1016/j.ijmedinf.2025.105838

Chenais, G., Lagarde, E., & Gil-Jardiné, C. (2023). Artificial intelligence in emergency medicine: Viewpoint of current applications and foreseeable opportunities and challenges. Journal of Medical Internet Research, 25. https://doi.org/10.2196/40031

Al-Dhubaibi, M. S., Mohammed, G. F., Atef, L. M., Bahaj, S. S., Al-Dhubaibi, A. M., & Bukhari, A. M. (2025). Artificial intelligence in aesthetic medicine: Applications, challenges, and future directions. Journal of Cosmetic Dermatology, 24(6). https://doi.org/10.1111/jocd.70241

Frank, K., Day, D., Few, J., Chiranjiv, C., Gold, M., Sattler, S., et al. (2024). AI assistance in aesthetic medicine: A consensus on objective medical standards. Journal of Cosmetic Dermatology, 23(12), 4110–4115. https://doi.org/10.1111/jocd.16481

Topsakal, O., Glinton, J., Celikoyar, M. M., et al. (2023). Open-source 3D morphing software for facial plastic surgery and facial landmark detection research and open access face data set based on deep learning (artificial intelligence) generated synthetic 3D models. Facial Plastic Surgery & Aesthetic Medicine, 26(2). https://doi.org/10.1089/fpsam.2023.0030

Panteli, D., Adib, K., Buttigieg, S., Goiana-da-Silva, F., Ladewig, K., Azzopardi-Muscat, N., Figueras, J., Novillo-Ortiz, D., & McKee, M. (2025). Artificial intelligence in public health: Promises, challenges, and an agenda for policy makers and public health institutions. Lancet Public Health, 10(5). https://doi.org/10.1016/S2468-2667(25)00036-2

Wang, X., He, X., Wei, J., Liu, J., Li, Y., & Liu, X. (2023). Application of artificial intelligence to the public health education. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1087174

Saw, S. N., & Ng, K. H. (2022). Current challenges of implementing artificial intelligence in medical imaging. Physics in Medicine, 100, 12–17. https://doi.org/10.1016/j.ejmp.2022.06.003

Bracciale, L., Loreti, P., & Bianchi, G. (2023). Cybersecurity vulnerability analysis of medical devices purchased by national health services. Scientific Reports, 13. https://doi.org/10.1038/s41598-023-45927-1

Alvarado, W., & Triantis, K. (2024). Human error in data breaches of electronic health records (EHR): Systematic literature review. Journal of Industrial Engineering and Management Studies, 11(1), 19–40. https://doi.org/10.22116/jiems.2024.418211.1533

Hanna, M. G., Pantanowitz, L., Dash, R., Deebajah, M., Pantanowitz, J., & Rashidi, H. H. (2025). Future of artificial intelligence—Machine learning trends in pathology and medicine. Modern Pathology. Advance online publication. https://doi.org/10.1016/j.modpat.2025.100705

Namazi, H., & Radfar, M. M. (2025). Philosophy of medicine meets AI hallucination and AI drift: Moving toward a more gentle medicine. Journal of Medical Ethics and History of Medicine, 18(2). https://doi.org/10.18502/jmehm.v18i2.18812

Dietrich, N. (2025). Agentic AI in radiology: Emerging potential and unresolved challenges. British Journal of Radiology, 98(1174), 1582–1584. https://doi.org/10.1093/bjr/tqaf173

Bergquist, M., Rolandsson, B., Gryska, E., Laesser, M., Hoefling, N., Heckemann, R., et al. (2023). Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology. European Radiology, 34(1), 338–347. https://doi.org/10.1007/s00330-023-09967-5

Rossa, J., Hammouche, S., Chen, Y., Rockall, A. G., & Royal College of Radiologists AI Working Group. (2024). Beyond regulatory compliance: Evaluating radiology artificial intelligence applications in deployment. Clinical Radiology, 79(5), 338–345. https://doi.org/10.1016/j.crad.2024.01.026

Cheng, J. Y., Abel, J. T., Balis, U. G. J., McClintock, D. S., & Pantanowitz, L. (2021). Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. American Journal of Pathology, 191(10), 1684–1692. https://doi.org/10.1016/j.ajpath.2020.10.018

Zalewa, K., Olszak, J., Kapłan, W., Orłowska, D., Bartoszek, L., Kaus, M., & Klepacz, N. (2025). Application of artificial intelligence in radiological image analysis for pulmonary disease diagnosis: A review of current methods and challenges. Journal of Education, Health and Sport, 77. https://doi.org/10.12775/JEHS.2025.77.56893

Zhang, X. M., Gao, T. H., Cai, Q. Y., Xia, J. B., Sun, Y. N., Yang, J., et al. (2026). Artificial intelligence in digital pathology diagnosis and analysis: Technologies, challenges, and future prospects. Military Medical Research, 12. https://doi.org/10.1186/s40779-025-00680-6

Debnath, J. (2023). Radiology in the era of artificial intelligence (AI): Opportunities and challenges. Medical Journal Armed Forces India, 79(4), 369–372. https://doi.org/10.1016/j.mjafi.2023.05.003

Amiot, F., & Potier, B. (2025). Artificial intelligence (AI) and emergency medicine: Balancing opportunities and challenges. JMIR Medical Informatics, 13. https://doi.org/10.2196/70903

Thunga, S., Khan, M., Cho, S. I., Na, J. I., & Yoo, J. (2024). AI in aesthetic/cosmetic dermatology: Current and future. Journal of Cosmetic Dermatology, 24(1). https://doi.org/10.1111/jocd.16640

Venturini, P., Lobato Faria, P., & Cordeiro, J. V. (2025). AI and omics technologies in biobanking: Applications and challenges for public health. Public Health, 243. https://doi.org/10.1016/j.puhe.2025.105726

Rojas-Carabali, W., Cifuentes-González, C., Gutierrez-Sinisterra, L., Heng, L. Y., Tsui, E., Gangaputra, S., et al. (2024). Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives. Asia-Pacific Journal of Ophthalmology, 13(4). https://doi.org/10.1016/j.apjo.2024.100082

Petrella, R. J. (2024). The AI future of emergency medicine. Annals of Emergency Medicine, 84, 139–153. https://doi.org/10.1016/j.annemergmed.2024.01.031

Kennet, P., & Falkner, S. (2025). The ethical frontier: AI in medicine and dentistry. Journal of Medical and Clinical Case Reports, 2(2). https://doi.org/10.61615/JMCCR/2025/MAY027140516

Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2). https://doi.org/10.3390/healthcare12020125

Marcus, E., & Teuwen, J. (2024). Artificial intelligence and explanation: How, why, and when to explain black boxes. European Journal of Radiology, 173. https://doi.org/10.1016/j.ejrad.2024.111393

Shoghli, A., Darvish, M., & Sadeghian, Y. (2024). Balancing innovation and privacy: Ethical challenges in AI-driven healthcare. Journal of Review of Medical Sciences, 4(1). https://doi.org/10.22034/jrms.2024.494112.1034

Edwards, C., Murphy, A., Singh, A., Daniel, S., & Chamunyonga, C. (2025). The role of patient outcomes in shaping moral responsibility in AI-supported decision making. Radiography, 31(3). https://doi.org/10.1016/j.radi.2025.102948