IJSDIP | International Journal of System Design & Info Processing

AI-Powered Clinical Decision Support System on AWS: A Review of Current Trends and Challenges

Paper ID:

IJSDIP_2023_VOLUME 11_ISSUE 4_286-293

Author's Name:

Sagar Kukkamudi, Divyasri Nalimela

Volume:

Issue:

Issue 4

Year:

2023

Pages:

286-293

Abstract

Clinical Decision Support Systems (CDSS) are changing the face of contemporary healthcare using Artificial Intelligence (AI) to help improve the accuracy of diagnostic data, treatment planning, and patient outcome prediction. As more healthcare organizations implement cloud computing, especially Amazon Web Services (AWS), they are implementing scalable, secure, and real-time AI-based solutions to CDSS. A thorough overview of the latest trends in the AI-powered CDSS designs deployed on AWS, such as machine learning-based diagnostics, real-time data analytics, serverless healthcare applications, and the interoperability of standardized healthcare data formats like HL7 FHIR, will be introduced in the paper. The paper also discusses technical, ethical, regulatory, and operational issues such as data privacy issues, model explainability, latency, cost control, AI bias, and adherence to medical policies such as the HIPAA. An AI-powered CDSS A conceptual AI-based architecture of the system is suggested, which includes the following services: Amazon SageMaker to develop the models, the Amazon Lambda to process them in a serverless manner, Amazon S3 to store the data safely, and Amazon HealthLake to manage the structured healthcare data. Findings of the recent implementations show that it has resulted in better diagnostic accuracy, less clinical workload, and increased scalability. Nevertheless, useful constraints like the cost of infrastructure, complexity of integration with the existing hospital infrastructure, explainability, and regulatory risk are considerable obstacles to mass adoption. Future research areas are federated learning to enable AI privacy, incorporating explainable AI (XAI), multi-cloud hybrid, and real-time IoT-based clinical monitoring frameworks.

Keywords

Clinical Decision Support System (CDSS), Cloud computing, Amazon Web Services (AWS), Healthcare analytics, Explainable artificial intelligence (XAI), Data privacy, HIPAA Compliance.

References

[1] Alagarswamy, K., Shi, W., Boini, A., Messaoudi, N., Grasso, V., Cattabiani, T., Turner, B., Croner, R., Kahlert, U. D., &Gumbs, A. (2021). Should AI-Powered Whole-Genome sequencing be used routinely for personalized decision support in surgical Oncology—A scoping review. BioMedInformatics, 4(3), 1757–1772. https://doi.org/10.3390/biomedinformatics4030096 [2] Das, D. C., &Akter, M. S. (2020). AI-Powered Multimodal diagnostics in modern healthcare: A shift toward integrative intelligence. Asia Pacific Journal of Medical Innovations, 2(3), 47–57. https://doi.org/10.70818/apjmi.v02i03.027 [3] Gandjour, A., Apel, C., Kendzia, D., Neri, L., Bellocchio, F., Usvyat, L., Larkin, J., &Vorkapic, J. P. (2020). Health-economic evaluation of an AI-powered decision support system for anemia management in in-centerhemodialysis patients. BMC Nephrology, 26(1), 496. https://doi.org/10.1186/s12882-025-04298-7 [4] Gupta, R., Goel, N., &Goel, N. (2022). 118. OPTIMIZING CLINICAL DECISION-MAKING AND PATIENT SATISFACTION IN MENTAL HEALTHCARE DELIVERY THROUGH AI-POWERED DIGITAL ASSISTANTS. American Journal of Geriatric Psychiatry, 33(10), S86–S87. https://doi.org/10.1016/j.jagp.2025.04.120 [5] Jadhav, D., Raut, Y., Shinde, D., &Borde, D. (2022, October 13). AI powered Early Detection of diabetic retinopathy: A Deep Learning Approach for improved Clinical Decision-Making. https://verjournal.com/index.php/ver/article/view/221 [6] Labinsky, H., Ukalovic, D., Hartmann, F., Runft, V., Wichmann, A., Jakubcik, J., Gambel, K., Otani, K., Morf, H., Taubmann, J., Fagni, F., Kleyer, A., Simon, D., Schett, G., Reichert, M., &Knitza, J. (2020). An AI-Powered Clinical Decision Support System to predict flares in rheumatoid arthritis: a pilot study. Diagnostics, 13(1), 148. https://doi.org/10.3390/diagnostics13010148 [7] Meyer, B., Kfuri‐Rubens, R., Schmidt, G., Tariq, M., Riedel, C., Recker, F., Riedel, F., Kiechle, M., & Riedel, M. (2022). Exploring the potential of AI ‐powered applications for clinical decision‐making in gynecologic oncology. International Journal of Gynecology& Obstetrics, 171(2), 698–704. https://doi.org/10.1002/ijgo.70251 [8] Nayak, A., Satpathy, I., &Khang, A. (2021). Employing AI-powered clinical decision support to revolutionize healthcare: Integrating data analytics with informed diagnostics. In Elsevier eBooks (pp. 433–446). https://doi.org/10.1016/b978-0-443-36434-1.00030-6 [9] Ouanes, K., &Farhah, N. (2022). Effectiveness of artificial intelligence (AI) in clinical decision support systems and care delivery. Journal of Medical Systems, 48(1), 74. https://doi.org/10.1007/s10916-024-02098-4 [10] Ratta, R., Sodhi, J., &Saxana, U. (2022). The relevance of trust in the implementation of AI-Driven Clinical Decision Support Systems by healthcare professionals: an extended UTAUT model. Electronic Journal of Knowledge Management, 23(1), 47–66. https://doi.org/10.34190/ejkm.23.1.3499 [11] Shah, C., Davtyan, K., Nasrallah, I., Bryan, R. N., & Mohan, S. (2022). Artificial Intelligence-Powered Clinical Decision Support and Simulation Platform for radiology trainee education. Journal of Digital Imaging, 36(1), 11–16. https://doi.org/10.1007/s10278-022-00713-9 [12] Soumma, S. B., Mamun, A., &Ghasemzadeh, H. (2022). AI-powered wearable sensors for health monitoring and clinical decision making. Current Opinion in Biomedical Engineering, 36, 100628. https://doi.org/10.1016/j.cobme.2025.100628 [13] Tanguay-Sela, M., Benrimoh, D., Popescu, C., Perez, T., Rollins, C., Snook, E., Lundrigan, E., Armstrong, C., Perlman, K., Fratila, R., Mehltretter, J., Israel, S., Champagne, M., Williams, J., Simard, J., Parikh, S. V., Karp, J. F., Heller, K., Linnaranta, O., . . . Margolese, H. C. (2021). Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for [14] depression treatment using a simulation center. Psychiatry Research, 308, 114336. https://doi.org/10.1016/j.psychres.2021.114336 Xu, Y., Fan, S., & Jiang, H. (2020). MediHC: An AI-powered framework for hierarchical disease classification using multi-head attention and contrastive learning. Decision Support Systems, 203, 114592. https://doi.org/10.1016/j.dss.2025.114592 [15] Yuan, S., Yang, Z., Li, J., Wu, C., & Liu, S. (2022). AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis. BMC Medical Informatics and Decision Making, 25(1), 203. https://doi.org/10.1186/s12911-025-03048-x

DOI: https://doi.org/10.64971/j.cph.ijsdip.v11.i4.1.2023