Understanding the Advancement of Artificial Intelligence in Healthcare
Keywords:
Artificial Intelligence, Federated Learning, Healthcare, Disease Prediction, Protein StructureAbstract
Artificial Intelligence (AI) has seen tremendous progress over the years. This growth in AI technology has significantly impacted almost every industry. One of the major beneficiaries of this rapid expansion in AI knowledge is the healthcare industry. This review presents a structured and evolutionary compilation of AI applications in the healthcare domain, tracing three major eras of AI: traditional machine learning models, deep learning models and foundational and multimodal models. This review focuses on representative empirical studies that introduced paradigm shifts in modeling, validation, and deployment. We analyze key application domains including diagnostics, patient management, operational efficiency, and biomedical discovery, while critically examining methodological limitations across eras.
Downloads
References
Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous Intelligence for Complex Goals - A Comprehensive Survey. IEEE Access, 13, 18912–18936. https://doi.org/10.1109/ACCESS.2025.3532853
Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019). Publicly Available Clinical BERT Embeddings. In A. Rumshisky, K. Roberts, S. Bethard, & T. Naumann (Eds.), Proceedings of the 2nd Clinical Natural Language Processing Workshop (pp. 72–78). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-1909
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., … Liang, P. (2022). On the Opportunities and Risks of Foundation Models. http://arxiv.org/abs/2108.07258
Bron, E. E., Smits, M., Niessen, W. J., & Klein, S. (2015). Feature Selection Based on the SVM Weight Vector for Classification of Dementia. IEEE Journal of Biomedical and Health Informatics, 19 (5), 1617–1626. https://doi.org/10.1109/JBHI.2015.2432832
Cameron, D., Bhagwan, V., & Sheth, A. P. (2012). Towards comprehensive longitudinal healthcare data capture. Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012, 240–247. https://doi.org/10.1109/BIBMW.2012.6470310
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine 2019 25:8, 25 (8), 1301–1309. https://doi.org/10.1038/s41591-019-0508-1
Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2017). GRAM: Graph-based attention model for healthcare representation learning. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F129685, 787–795. https://doi.org/10.1145/3097983.3098126
Choi, E., Taha Bahadori, M., Kulas, J. A., Schuetz, A., Stewart, W. F., Sun, J., & Health, S. (2016). RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. Advances in Neural Information Processing Systems, 29.
Chou, R. H., Hsu, B. W. Y., Yu, C. L., Chen, T. Y., Ou, S. M., Lee, K. H., Tseng, V. S., Huang, P. H., & Tarng, D. C. (2024). Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center. Journal of the Chinese Medical Association : JCMA, 87(4), 369. https://doi.org/10.1097/JCMA.0000000000001066
Davenport, T., & 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
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., & Ranjan, R. (2023). Explainable AI (XAI): Core Ideas, Techniques, and Solutions. ACM Computing Surveys, 55 (9). https://doi.org/10.1145/3561048
Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14 (2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
Fagerström, J., Bång, M., Wilhelms, D., & Chew, M. S. (2019). LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock. Scientific Reports 2019 9:1, 9 (1), 15132-. https://doi.org/10.1038/s41598-019-51219-4
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines 2018 28:4, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36 (4), 193–202. https://doi.org/10.1007/BF00344251
Futoma, J., Morris, J., & Lucas, J. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, 56 (9), 229–238. https://doi.org/10.1016/j.jbi.2015.05.016
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9
Goldberg, S. I., Shubina, M., Niemierko, A., & Turchin, A. (2010). A Weighty Problem: Identification, Characteristics and Risk Factors for Errors in EMR Data. AMIA Annual Symposium Proceedings, 2010, 251. https://pmc.ncbi.nlm.nih.gov/articles/PMC3041371/
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., & Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316 (22), 2402–2410. https://doi.org/10.1001/jama.2016.17216
Gunning, D. (2016). Explainable Artificial Intelligence (XAI).
Hasan, M., Fukuda, A., Maruf, R. I., Yokota, F., & Ahmed, A. (2017). Errors in remote healthcare system: Where, how and by whom? IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017-December, 170–175. https://doi.org/10.1109/TENCON.2017.8227856
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16 (1), 45–74. https://doi.org/10.1007/s12559-023-10179-8
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Horikawa, T. (2025). Mind captioning: Evolving descriptive text of mental content from human brain activity. Science Advances , 11 (45). https://doi.org/10.1126/sciadv.adw1464
Jin, W., Barzilay, Dr. R., & Jaakkola, T. (2020). Hierarchical Generation of Molecular Graphs using Structural Motifs. In H. D. III & A. Singh (Eds.), Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 4839–4848). PMLR. https://proceedings.mlr.press/v119/jin20a.html
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 2021 596:7873, 596 (7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
Kalra, N., Verma, P., & Verma, S. (2024). Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques. Computers in Biology and Medicine, 179(7), 108917. https://doi.org/10.1016/j.compbiomed.2024.108917
Karunanayake, N. (2025). Next-generation agentic AI for transforming healthcare. Informatics and Health, 2(2), 73–83. https://doi.org/https://doi.org/10.1016/j.infoh.2025.03.001
Kitsios, F., Kamariotou, M., Syngelakis, A. I., & Talias, M. A. (2023). Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review. Applied Sciences 2023, Vol. 13, Page 7479, 13(13), 7479. https://doi.org/10.3390/app13137479
Kuwaiti, A., Nazer, A. ;, Al-Reedy, K. ;, Al-Shehri, A. ;, Al-Muhanna, S. ;, Subbarayalu, A. ;, Al Muhanna, A. V. ;, Al-Muhanna, D. ;, Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023). A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine 2023, Vol. 13, Page 951, 13(6), 951. https://doi.org/10.3390/jpm13060951
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1 (4), 541–551. https://doi.org/10.1162/neco.1989.1.4.541
Liang, S., Singh, M., Dharmaraj, S., & Gam, L. H. (2010). The PCA and LDA analysis on the differential expression of proteins in breast cancer. Disease Markers, 29 (5), 231–242. https://doi.org/10.3233/DMA-2010-0753
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., & Rives, A. (2022). Language models of protein sequences at the scale of evolution enable accurate structure prediction. BioRxiv, 2022.07.20.500902. https://doi.org/10.1101/2022.07.20.500902
Ling, Y., An, Y., Liu, M., & Hu, X. (2013). An error detecting and tagging framework for reducing data entry errors in electronic medical records (EMR) system. Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, 249–254. https://doi.org/10.1109/BIBM.2013.6732498
Liu, S.-H., Cheng, D.-C., & Lin, C.-M. (2013). Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System. Sensors, 13 (1), 813–828. https://doi.org/10.3390/s130100813
Moor, M., Huang, Q., Wu, S., Yasunaga, M., Dalmia, Y., Leskovec, J., Zakka, C., Pontes, E., Hospital, R., Einstein, I. A., Paulo, S., & Pranav Rajpurkar, B. (2023). Med-Flamingo: a Multimodal Medical Few-shot Learner. In Proceedings of Machine Learning Research (Vol. 225).
Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G. (2018). An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine, 46 (4), 547–553. https://doi.org/10.1097/CCM.0000000000002936
Patel, M. M., Rathod, A., & Shah, M. N. (2025). Graph Neural Networks Vs Transformers: A Comparative Study on Word Sense Disambiguation. International Research Journal of Engineering and Technology. www.irjet.net
Polat, K., Şahan, S., & Güneş, S. (2007). Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Expert Systems with Applications, 32 (2), 625–631. https://doi.org/10.1016/j.eswa.2006.01.027
Pradhan, V., Shekhar, H., Kumari Munda, P., Tiwari, A. K., Jha, S., Rai, P., & Affiliations, ’. (2026). Accuracy of Artificial Intelligence-Based Models versus Traditional Scoring Systems (APACHE, SOFA, SAPS) for Predicting Mortality in ICU Patients: A Systematic Review and Meta-Analysis. MedRxiv, 2026.01.14.26344000. https://doi.org/10.64898/2026.01.14.26344000
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of Machine Learning Research, 139, 8748–8763. http://arxiv.org/abs/2103.00020
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. http://arxiv.org/abs/1711.05225
Reddy, G. P., Pavan Kumar, Y. V., & Prakash, K. P. (2024). Hallucinations in Large Language Models (LLMs). 2024 IEEE Open Conference of Electrical, Electronic and Information Sciences, EStream 2024 - Proceedings. https://doi.org/10.1109/eStream61684.2024.10542617
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. Npj Digital Medicine 2020 3:1, 3 (1), 119-. https://doi.org/10.1038/s41746-020-00323-1
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 2019 1:5, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Samuel, A. L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 210–229. https://doi.org/10.1147/rd.33.0210
Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., Chen, J., Mahvar, F., Yatziv, L., Chen, T., Sterling, B., Baby, S. A., Baby, S. M., Lai, J., Schmidgall, S., … Yang, L. (2025). MedGemma Technical Report. http://arxiv.org/abs/2507.05201
Shen, Y., Yu, J., Zhou, J., & Hu, G. (2025). Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review. Journal of Medical Internet Research, 27, e59024. https://doi.org/10.2196/59024
Suura, S. R. (2025). Issue 4s (2025) Sambasiva Rao Suura, (2025) Agentic AI Systems in Organ Health Management: Early Detection of Rejection in Transplant Patients. Journal of Neonatal Surgery ISSN, 14(4s), 490–500. https://www.jneonatalsurg.com
The 2025 AI Index Report | Stanford HAI. (2025). https://hai.stanford.edu/ai-index/2025-ai-index-report
Tinauer, C., Heber, S., Pirpamer, L., Damulina, A., Schmidt, R., Stollberger, R., Ropele, S., & Langkammer, C. (2022). Interpretable brain disease classification and relevance-guided deep learning. Scientific Reports 2022 12:1, 12 (1), 20254-. https://doi.org/10.1038/s41598-022-24541-7
Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In F. Doshi-Velez, J. Fackler, K. Jung, D. Kale, R. Ranganath, B. Wallace, & J. Wiens (Eds.), Proceedings of the 4th Machine Learning for Healthcare Conference (Vol. 106, pp. 359–380). PMLR. https://proceedings.mlr.press/v106/tonekaboni19a.html
Vaswani, A., Brain, G., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, 30.
Vatsal, S., Dubey, H., & Singh, A. (2026). Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3651218
Wang, X., Wei, Y., Xiong, Y., Huang, G., Qian, X., Ding, Y., Wang, M., & Li, L. (2022). LightSeq2: Accelerated Training for Transformer-Based Models on GPUs. International Conference for High Performance Computing, Networking, Storage and Analysis, SC, 2022-November. https://doi.org/10.1109/SC41404.2022.00043
Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, brian, Xia, F., Chi, E., Le, Q. V, & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems (Vol. 35, pp. 24824–24837). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf
Wolberg, E. J. (2010). The Method of Least Squares. In Designing Quantitative Experiments: Prediction Analysis (pp. 47–89). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-11589-9_3
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., Harle, C. A., Lipori, G., Mitchell, D. A., Hogan, W. R., Shenkman, E. A., Bian, J., & Wu, Y. (2022). GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records. https://arxiv.org/abs/2203.03540
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., & Narasimhan, K. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems (Vol. 36, pp. 11809–11822). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2023/file/271db9922b8d1f4dd7aaef84ed5ac703-Paper-Conference.pdf
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. http://arxiv.org/abs/2210.03629
Ye, M., Fang, X., Du, B., Yuen, P. C., & Tao, D. (2024). Heterogeneous Federated Learning: State-of-the-art and Research Challenges. ACM Computing Surveys, 56 (3). https://doi.org/10.1145/3625558
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018). Predicting Diabetes Mellitus With Machine Learning Techniques. Frontiers in Genetics, 9, 416440. https://doi.org/10.3389/fgene.2018.00515
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sanjana Yadav, Megha Khanna (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License which permits
its use, distribution and reproduction in any medium, provided the original work is cited.