A Review on the Role of Artificial Intelligence in Medicine and Clinical Sciences
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I2P101Keywords:
Artificial Intelligence in Healthcare, Clinical Decision Support Systems, Medical Data Analytics and Machine Learning, Ethical and Explainable AI in Medicine, Predictive and Personalized Clinical CareAbstract
The challenges posed by artificial intelligence (AI) in health and clinical sciences is extremely rapid and transformative in the prevention, diagnosis, treatment, and planning, and future management of diseases. In clinical decision-making, medical imaging, and analysis, data-driven systems, personalized medicine, and AI are being deployed (Bathija et al., 2026). Although these widespread innovations have taken place in medical AI, the most prevalent AI system in healthcare still lacks the fundamental requirements of the healthcare domain, such as interpretability, data accessibility, real-time response, transparency, and alignment. These challenges underscore the need for human safety, reliability, and trust when healthcare systems are deployed in the real world. Describing the AI role in fundamental areas of health and clinical sciences, the author identifies some patterns, applications and the gaps in the literature. The author goes beyond summarizing the literature, tackling some of the new conceptual issues that have not been addressed before. First, the author develops what may be called a Human-Intuition-Augmented AI Clinical Framework, where the frosted reasoning of a clinician is mathematically captured in the AI learning loop. Secondly, the author coins a new term, which is, Context-Aware Ethical Intelligence. In this case, the author suggests that an AI system be able to shift the boundary of ethical decisions to a given patient and the culture surrounding him/her. Third, in the case of a Clinical Knowledge Evolution Model, the author suggests that AI systems be enabled to self-update the medical knowledge that they have acquired post-deployment without having to be retrained from scratch. Lastly, the author proposes a new set of Clinical Intelligence that Transcends Domains, incorporating AI fusion of the genome, behavior, environment, and psychosocial data, and proposes to equip clinical intelligence layers with cross-domain functionalities (Elinjulliparambil & Rathod, 2026). Such proposals will advance the frontier of AI in smart healthcare systems.
References
[1] Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., ... & Al-Muhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of personalized medicine, 13(6), 951.
[2] Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., ... & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689.
[3] Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Aussó, S., Alberich, L. C., Marias, K., Tsiknakis, M., Colantonio, S., Papanikolaou, N., Salahuddin, Z., Woodruff, H. C., Lambin, P., & Martí-Bonmatí, L. (2021). FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. arXiv. https://arxiv.org/abs/2109.09658
[4] Bernstam, E. V., Shireman, P. K., Meric‐Bernstam, F., N Zozus, M., Jiang, X., Brimhall, B. B., ... & Becich, M. J. (2022). Artificial intelligence in clinical and translational science: Successes, challenges and opportunities. Clinical and translational science, 15(2), 309-321.
[5] Bhardwaj, A., Kishore, S., & Pandey, D. K. (2022). Artificial intelligence in biological sciences. Life, 12(9), 1430.
[6] Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021). The state of the art of deep learning models in medical science and their challenges. Multimedia Systems, 27(4), 599-613.
[7] Blaizot, A., Veettil, S. K., Saidoung, P., Moreno‐Garcia, C. F., Wiratunga, N., Aceves‐Martins, M., ... & Chaiyakunapruk, N. (2022). Using artificial intelligence methods for systematic review in health sciences: A systematic review. Research Synthesis Methods, 13(3), 353-362.
[8] Busnatu, Ș., Niculescu, A. G., Bolocan, A., Petrescu, G. E., Păduraru, D. N., Năstasă, I., ... & Martins, H. (2022). Clinical applications of artificial intelligence—an updated overview. Journal of clinical medicine, 11(8), 2265.
[9] Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., ... & Jiang, Y. (2022). Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey. Frontiers in medicine, 9, 990604.
[10] Elinjulliparambil, S. H. (2023). Real-Time Instance Segmentation Using Lightweight CNN-Transformer Hybrids.
[11] Elinjulliparambil, S. H. (2024). Vision-Based Human Action Recognition Using Skeleton Graph Neural Networks.
[12] Elinjulliparambil, S. H. (2025). Generalist Vision Models for Any-to-Any Image-to-Video Understanding.
[13] Yigitcanlar, T., & Cugurullo, F. (2020). The sustainability of artificial intelligence: An urbanistic viewpoint from the lens of smart and sustainable cities. Sustainability, 12(20), 8548. https://doi.org/10.3390/su12208548
[14] Gandhi, K., & Verma, P. (2024). AI-enhanced outage prediction and restoration planning for storm and extreme-weather events. International Journal of AI, Big Data, Computational and Management Studies, 5(2), 180–191.
[15] Gandhi, K., Verma, P., Govindarajan, V., & Sonani, R. (2025). Advancing software maintenance with LLMs and cloud-based deep learning.
[16] Garg, R., Patel, A., & Hoda, W. (2021). Emerging role of artificial intelligence in medical sciences—Are we ready!. Journal of Anaesthesiology Clinical Pharmacology, 37(1), 35-36.
[17] Iqbal, J., Cortés Jaimes, D. C., Makineni, P., Subramani, S., Hemaida, S., Thugu, T. R., ... & Arain, M. (2023). Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus, 15(9), e44658.
[18] Khan, R. A., Jawaid, M., Khan, A. R., & Sajjad, M. (2023). ChatGPT-Reshaping medical education and clinical management. Pakistan journal of medical sciences, 39(2), 605.
[19] Liu, P. R., Lu, L., Zhang, J. Y., Huo, T. T., Liu, S. X., & Ye, Z. W. (2021). Application of artificial intelligence in medicine: an overview. Current Medical Science, 41(6), 1105-1115.
[20] Manickam, P., Mariappan, S. A., Murugesan, S. M., Hansda, S., Kaushik, A., Shinde, R., & Thipperudraswamy, S. P. (2022). Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors, 12(8), 562.
[21] Pasham, S. D. (2023). The function of artificial intelligence in healthcare: a systematic literature review. International Journal of Acta Informatica, 2(1), 32-42.
[22] Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Can artificial intelligence help for scientific writing?. Critical care, 27(1), 75.
[23] Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 21(1), 125.
[24] Shaheen, M. Y. (2021). Applications of Artificial Intelligence (AI) in healthcare: A review. ScienceOpen Preprints.
[25] Tian, M., Shen, Z., Wu, X., Wei, K., & Liu, Y. (2023). The application of artificial intelligence in medical diagnostics: A new frontier. Academic Journal of Science and Technology, 8(2), 57-61.
[26] van de Sande, D., van Genderen, M. E., Huiskens, J., Gommers, D., & van Bommel, J. (2021). Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive care medicine, 47(7), 750-760.
[27] Van Dijk, S. H., Brusse-Keizer, M. G., Bucsan, C. C., van der Palen, J., Doggen, C. J., & Lenferink, A. (2023). Artificial intelligence in systematic reviews: promising when appropriately used. BMJ open, 13(7), e072254.
[28] Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., ... & McCulloch, P. (2022). Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. bmj, 377.
[29] Yan, Y., Hu, Z., Yuan, W., & Wang, J. (2022). Pipeline leak detection based on empirical mode decomposition and deep belief network. Measurement and Control, 56(1–2), 182–193. https://doi.org/10.1177/00202940221088713
[30] Wang, L., Zhang, Y., Wang, D., Tong, X., Liu, T., Zhang, S., ... & Clarke, M. (2021). Artificial intelligence for COVID-19: a systematic review. Frontiers in medicine, 8, 704256.
[31] Xie, Y., Lu, L., Gao, F., He, S. J., Zhao, H. J., Fang, Y., ... & Dong, Z. (2021). Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare. Current medical science, 41(6), 1123-1133.
[32] Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4).
[33] Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of artificial intelligence applications in real-life clinical practice: systematic review. Journal of medical Internet research, 23(4), e25759.
