A Modality-Adaptive Software Architecture for Explainable Clinical Decision Support

Authors

DOI:

https://doi.org/10.66395/globeis.25

Keywords:

clinical decision support, software architecture, explainable artificial intelligence, medical imaging, health information systems

Abstract

Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) increasingly combine structured clinical data and medical images, but their explanations differ in cost, format, storage, and failure behavior. This study presents a modality-adaptive software architecture for a browser-based CDSS supporting 10-year coronary heart disease (CHD) prediction, 30-day hospital readmission prediction, and chest-radiograph classification with 14 independent output scores. The architecture treats explanation selection as a request-time service policy: Shapley additive explanations (SHAP) accompany tabular predictions; local interpretable model-agnostic explanations (LIME) are requested separately; gradient-weighted class activation mapping (Grad-CAM) and its generalized variant (Grad-CAM++) are computed only for threshold-positive classes within the five highest-ranked scores; and large language model (LLM) narratives are attempted when configured. React, FastAPI, PostgreSQL, and a seven-day Redis cache implement the design. Seven synthetic fixture families satisfied the specified selection conditions. In a backend experiment involving 7,818 radiographs, heatmap generation averaged 140.2 ms per radiograph, or 78.2% of the 179.3-ms mean total. A separate in-process service-layer test completed all 3,600 requests successfully using deterministic prediction and explanation substitutes and SQLite persistence. The architecture specifies bounded explanation work and separate retention paths. Full-stack performance, clinical-model calibration, explanation quality, and clinician benefit remain subjects for future evaluation.

Downloads

Download data is not yet available.

Author Biographies

  • Danila Lissovoy, Nazarbayev University

    Department of Computer Science, School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan

  • Assylbek Khalyk, Nazarbayev University

    Department of Computer Science, School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan

  • Akylbek Oralov, Nazarbayev University
    Department of Computer Science, School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan
  • Rakhat Mustafin, Nazarbayev University
    Department of Computer Science, School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan
  • Alisher Kenzhegaliyev, Nazarbayev University
    Department of Computer Science, School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan
  • Hashim Ali, Nazarbayev University
    Department of Computer Science, School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan

References

1. Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., Kaissis, G., Tsakou, G., Buvat, I., Kalpathy-Cramer, J., Mongan, J., Schnabel, J. A., Kushibar, K., Riklund, K., Marias, K., … Starmans, M. P. A. (2025). FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 388, e081554. https://doi.org/10.1136/bmj-2024-081554 DOI: https://doi.org/10.1136/bmj-2024-081554

2. Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., Liu, X., Mateen, B. A., Mathur, P., McCradden, M. D., Morgan, L., Ordish, J., Rogers, C., Saria, S., Ting, D. S. W., … Perkins, Z. B. (2022). Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine, 28(5), 924–933. https://doi.org/10.1038/s41591-022-01772-9 DOI: https://doi.org/10.1136/bmj-2022-070904

3. Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., Van Smeden, M., Boulesteix, A.-L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., … Logullo, P. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, e078378. https://doi.org/10.1136/bmj-2023-078378 DOI: https://doi.org/10.1136/bmj-2023-078378

4. Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022). Multimodal Biomedical AI. Nature Medicine, 28(9), 1773–1784. https://doi.org/10.1038/s41591-022-01981-2 DOI: https://doi.org/10.1038/s41591-022-01981-2

5. Soenksen, L. R., Ma, Y., Zeng, C., Boussioux, L., Villalobos Carballo, K., Na, L., Wiberg, H. M., Li, M. L., Fuentes, I., & Bertsimas, D. (2022). Integrated Multimodal Artificial Intelligence Framework for Healthcare Applications. npj Digital Medicine, 5, 149. https://doi.org/10.1038/s41746-022-00689-4 DOI: https://doi.org/10.1038/s41746-022-00689-4

6. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, 4768–4777. https://dl.acm.org/doi/10.5555/3295222.3295230

7. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778 DOI: https://doi.org/10.1145/2939672.2939778

8. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision (ICCV), 618–626. https://doi.org/10.1109/ICCV.2017.74 DOI: https://doi.org/10.1109/ICCV.2017.74

9. Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 839–847. https://doi.org/10.1109/WACV.2018.00097 DOI: https://doi.org/10.1109/WACV.2018.00097

10. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018). Sanity checks for saliency maps. Advances in Neural Information Processing Systems 31, 9505–9515.

11. Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 180–186. https://doi.org/10.1145/3375627.3375830 DOI: https://doi.org/10.1145/3375627.3375830

12. Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware Explanations from AI in Health Care. Science, 373(6552), 284–286. https://doi.org/10.1126/science.abg1834 DOI: https://doi.org/10.1126/science.abg1834

13. 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 DOI: https://doi.org/10.1016/S2589-7500(21)00208-9

14. Ali, H. (2026). Causal-Pathway-Guided DNN–GBDT Distillation for Interpretable Artificial Intelligence in Intensive Care Units. Informatics, 13(8), 124. https://doi.org/10.3390/informatics13080124 DOI: https://doi.org/10.3390/informatics13080124

15. Chen, H., Gomez, C., Huang, C.-M., & Unberath, M. (2022). Explainable medical imaging AI needs human-centered design: Guidelines and evidence from a systematic review. npj Digital Medicine, 5, 156. https://doi.org/10.1038/s41746-022-00699-2 DOI: https://doi.org/10.1038/s41746-022-00699-2

16. Bayor, A. A., Li, J., Yang, I. A., & Varnfield, M. (2025). Designing Clinical Decision Support Systems (CDSS)—A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review. Journal of Medical Internet Research, 27, e63733. https://doi.org/10.2196/63733 DOI: https://doi.org/10.2196/63733

17. Calem, J., Dias, J., & Nunes, N. J. (2024). Intelligent Systems in Healthcare: A Systematic Survey of Explainable User Interfaces. Computers in Biology and Medicine, 180, 108908. https://doi.org/10.1016/j.compbiomed.2024.108908 DOI: https://doi.org/10.1016/j.compbiomed.2024.108908

18. Van Dort, B. A., Engelsma, T., Sneekes, P., Peute, L., & Medlock, S. (2026). Explainable AI in Hospital Clinical Decision Support Systems: A Scoping Review of Healthcare Professionals’ Perspectives. PLOS Digital Health, 5(5), e0001417. https://doi.org/10.1371/journal.pdig.0001417 DOI: https://doi.org/10.1371/journal.pdig.0001417

19. Hur, S., Lee, Y., Park, J., Jeon, Y. J., Cho, J. H., Cho, D., Lim, D., Hwang, W., Cha, W. C., & Yoo, J. (2025). Comparison of SHAP and Clinician Friendly Explanations Reveals Effects on Clinical Decision Behaviour. npj Digital Medicine, 8, 578. https://doi.org/10.1038/s41746-025-01958-8 DOI: https://doi.org/10.1038/s41746-025-01958-8

20. Kayser, M., Menzat, B., Emde, C., Bercean, B., Novak, A., Espinosa, A., Papiez, B. W., Gaube, S., Lukasiewicz, T., & Camburu, O.-M. (2024). Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 18891–18919. https://doi.org/10.18653/v1/2024.emnlp-main.1051 DOI: https://doi.org/10.18653/v1/2024.emnlp-main.1051

21. Saporta, A., Gui, X., Agrawal, A., Pareek, A., Truong, S. Q. H., Nguyen, C. D. T., Ngo, V.-D., Seekins, J., Blankenberg, F. G., Ng, A. Y., Lungren, M. P., & Rajpurkar, P. (2022). Benchmarking Saliency Methods for Chest X-Ray Interpretation. Nature Machine Intelligence, 4(10), 867–878. https://doi.org/10.1038/s42256-022-00536-x DOI: https://doi.org/10.1038/s42256-022-00536-x

22. Brankovic, A., Cook, D., Rahman, J., Delaforce, A., Li, J., Magrabi, F., Cabitza, F., Coiera, E., & Bradford, D. (2025). Clinician-informed XAI evaluation checklist with metrics (CLIX-M) for AI-powered clinical decision support systems. npj Digital Medicine, 8(1), 364. https://doi.org/10.1038/s41746-025-01764-2 DOI: https://doi.org/10.1038/s41746-025-01764-2

23. Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023). Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis. Medical Image Analysis, 84, 102684. https://doi.org/10.1016/j.media.2022.102684 DOI: https://doi.org/10.1016/j.media.2022.102684

24. Rosenbacke, R., Melhus, Å., McKee, M., & Stuckler, D. (2024). How Explainable Artificial Intelligence Can Increase or Decrease Clinicians’ Trust in AI Applications in Health Care: Systematic Review. JMIR AI, 3, e53207. https://doi.org/10.2196/53207 DOI: https://doi.org/10.2196/53207

25. International Organization for Standardization. (2022). ISO/IEC/IEEE 42010:2022: Software, Systems and Enterprise—Architecture Description (ISO/IEC/IEEE 42010:2022). International Organization for Standardization. https://www.iso.org/standard/74393.html

26. International Organization for Standardization. (2023). ISO/IEC 25010:2023: Systems and software engineering—Systems and software Quality Requirements and Evaluation (SQuaRE)—Product quality model. https://www.iso.org/standard/78176.html

27. International Organization for Standardization. (2023). ISO/IEC 25059:2023: Software Engineering—Systems and Software Quality Requirements and Evaluation (SQuaRE)—Quality Model for AI Systems (ISO/IEC 25059:2023). International Organization for Standardization. https://www.iso.org/standard/80655.html

28. Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1 DOI: https://doi.org/10.6028/NIST.AI.100-1

29. Huang, J., & Liu, Y. (2025). MLXOps4Medic: A Service Framework for Machine Learning and Explainability Operations in Medical Imaging AI Development. IEEE Access, 13, 158149–158169. https://doi.org/10.1109/ACCESS.2025.3606838 DOI: https://doi.org/10.1109/ACCESS.2025.3606838

30. Yoo, J., Lee, J., Min, J. Y., Choi, S. W., Kwon, J.-M., Cho, I., Lim, C., Choi, M. Y., & Cha, W. C. (2022). Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study. Journal of Medical Internet Research, 24(7), e37928. https://doi.org/10.2196/37928 DOI: https://doi.org/10.2196/37928

31. HL7 International. (2023). FHIR Release 5: Provenance Resource. https://hl7.org/fhir/R5/provenance.html

32. D’Agostino, R. B., Sr., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008). General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation, 117(6), 743–753. https://doi.org/10.1161/CIRCULATIONAHA.107.699579 DOI: https://doi.org/10.1161/CIRCULATIONAHA.107.699579

33. Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., & Clore, J. N. (2014). Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records. BioMed Research International, 2014, 781670. https://doi.org/10.1155/2014/781670 DOI: https://doi.org/10.1155/2014/781670

34. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 3462–3471. https://doi.org/10.1109/CVPR.2017.369 DOI: https://doi.org/10.1109/CVPR.2017.369

35. 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 (arXiv:1711.05225). arXiv. https://doi.org/10.48550/arXiv.1711.05225

Downloads

Published

2026-10-23

Issue

Vol. 2 No. 1 (2026): June–November 2026

Section

Articles

License

Copyright (c) 2026 Danila Lissovoy, Assylbek Khalyk, Akylbek Oralov, Rakhat Mustafin, Alisher Kenzhegaliyev, Hashim Ali (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

A Modality-Adaptive Software Architecture for Explainable Clinical Decision Support. (2026). International Journal of Global Information Systems, 2(1), 162-182. https://doi.org/10.66395/globeis.25