Impact of Artificial Intelligence on Medical Diagnosis: A Systematic Literature Review
DOI:
https://doi.org/10.63688/brdxah60Keywords:
artificial intelligence, medical diagnosis, machine learning, deep learningAbstract
Artificial Intelligence (AI) has significantly transformed healthcare, particularly medical diagnostic processes, by providing tools capable of analyzing large volumes of clinical data, identifying complex patterns, and supporting healthcare decision-making. The aim of this study was to analyze the impact of Artificial Intelligence on medical diagnosis through a systematic review of the scientific literature. The methodology followed the PRISMA 2020 guidelines and included publications indexed in Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, IEEE Xplore, and Google Scholar between 2015 and 2025. After applying the inclusion and exclusion criteria, 20 studies were selected for the final analysis. The findings revealed that machine learning and deep learning have improved diagnostic accuracy across several medical specialties, particularly radiology, dermatology, ophthalmology, and cardiovascular medicine. Furthermore, AI was found to promote precision medicine through the integration of clinical and predictive information. Nevertheless, challenges remain regarding data quality, algorithm transparency, regulation, and ethical considerations associated with its implementation. It is concluded that Artificial Intelligence has considerable potential to optimize diagnostic processes and enhance healthcare services; however, its effective adoption requires clinical validation, professional oversight, and appropriate regulatory frameworks.
References
Badaró, S., Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505–515. https://doi.org/10.1148/rg.2017160130
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., et al. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy. JAMA, 316(22), 2402–2410. https://doi.org/10.1001/jama.2016.17216
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., et al. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Ker, J., Wang, L., Rao, J., & Lim, T. (2018). Deep learning applications in medical image analysis. IEEE Access, 6, 9375–9389. https://doi.org/10.1109/ACCESS.2017.2788044
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Litjens, G., Kooi, T., Bejnordi, B., Setio, A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
Murphy, K. P. (2022). Probabilistic machine learning: An introduction. MIT Press.
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. https://doi.org/10.1177/1609406917733847
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., et al. (2019). Artificial intelligence versus clinicians in disease diagnosis: Systematic review. Journal of Medical Internet Research, 21(8), e10010. https://doi.org/10.2196/10010
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
World Health Organization. (2021). Ethics and governance of artificial intelligence for health. World Health Organization. https://www.who.int/publications/i/item/9789240029200
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z
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Copyright (c) 2026 Miguel Ángel Poaquiza Aguilar, Paola Estefania Sisa García, Evelyn Johanna Pacha Jara, Gabriela Salomé Carrasco Yépez, Jimena Fernanda Ramírez Machado (Autor/a)
This work is licensed under a Creative Commons Attribution 4.0 International License.
