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Multimodal Artificial Intelligence for Disease Detection and Clinical Decision Support Using Medical Imaging, Electronic Health Records, and Clinical Data
Authors
1 2 3 5 6 Department of Computer Science Application, GIET University Gunupur, India. 4 Madanapalle Institute of Technology and Science University, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 85-91
- Article Type
- Research Article
References
1. Simon, B.; Ozyoruk, K.; Gelikman, D.; Harmon, S.; Türkbey, B. The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: A narrative review. Diagn. Interv. Radiol. 2024.
2. Laganà, F.; Bibbò, L.; Calcagno, S.; De Carlo, D.; Pullano, S.A.; Pratticò, D.; Angiulli, G. Smart Electronic Device-Based Monitoring of SAR and Temperature Variations in Indoor Human Tissue Interaction. Appl. Sci. 2025, 15, 2439.
3. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
4. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
5. Mario, V.; Laganá, F.; Manin, L.; Angiulli, G. Soft computing and eddy currents to estimate and classify delaminations in biomedical device CFRP plates. J. Electr. Eng. 2025, 76, 72–79.
6. Menniti, M.; Laganà, F.; Oliva, G.; Bianco, M.; Fiorillo, A.S.; Pullano, S.A. Development of Non-Invasive Ventilator for Homecare and Patient Monitoring System. Electronics 2024, 13, 790.
7. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1- 6). IEEE.
8. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
9. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
10. Li, Y.; Daho, M.E.H.; Conze, P.H.; Zeghlache, R.; Le Boité, H.; Tadayoni, R.; Cochener, B.; Lamard, M.; Quellec, G. A review of deep learning-based information fusion techniques for multimodal medical image classification. Comput. Biol. Med. 2024, 177, 108635.
11. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
12. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi- Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
13. Ruckert, J.; Bloch, L.; Brungel, R.; Idrissi-Yaghir, A.; Schäfer, H.; Schmidt, C.S.; Koitka, S.; Pelka, O.; Ben, A.; Abacha, A.G.; et al. ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset. Sci. Data 2024, 11, 688.
14. Li, Q.; Yang, Z.; Chen, K.; Zhao, M.; Long, H.; Deng, Y.; Hu, H.; Jia, C.; Wu, M.; Zhao, Z.; et al. Human-multimodal deep learning collaboration in ‘precise’diagnosis of lupus erythematosus subtypes and similar skin diseases. J. Eur. Acad. Dermatol. Venereol. 2024, 38, 2268–2279.
15. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
16. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
17. Reith, T.P.; D’Alessandro, D.M.; D’Alessandro, M.P. Capability of multimodal large language models to interpret pediatric radiological images. Pediatr. Radiol. 2024, 54, 1729–1737.
18. Martin, S.A.; Zhao, A.; Qu, J.; Imms, P.E.; Irimia, A.; Barkhof, F.; Cole, J.H.; Initiative, A.D.N. Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis. medRxiv 2025.
19. Mishra, S. R., & Dash, S. (2026). AI ‐ Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper ‐ Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
20. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211- 224). Cham: Springer Nature Switzerland.
21. Xue, C.; Kowshik, S.S.; Lteif, D.; Puducheri, S.; Jasodanand, V.H.;
22. Zhou, O.T.; Walia, A.S.; Guney, O.B.; Zhang, J.D.; Pham, S.T.; et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 2024, 30, 2977–2989.
23. Schilcher, J.; Nilsson, A.; Andlid, O.; Eklund, A. Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures. Comput. Biol. Med. 2024, 168, 107704.
24. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
25. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
26. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
27. Niu, S.; Ma, J.; Bai, L.; Wang, Z.; Guo, L.; Yang, X. EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation. Inf. Fusion 2024, 102, 102069.
28. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
29. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1- 8). IEEE.
30. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317 Zeng, L.; Ma, P.; Li, Z.; Liang, S.; Wu, C.; Hong, C.; Li, Y.; Cui, H.;
31. Li, R.; Wang, J.; et al. Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia. Adv. Sci. 2024, 11, 2404047.
2. Laganà, F.; Bibbò, L.; Calcagno, S.; De Carlo, D.; Pullano, S.A.; Pratticò, D.; Angiulli, G. Smart Electronic Device-Based Monitoring of SAR and Temperature Variations in Indoor Human Tissue Interaction. Appl. Sci. 2025, 15, 2439.
3. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
4. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
5. Mario, V.; Laganá, F.; Manin, L.; Angiulli, G. Soft computing and eddy currents to estimate and classify delaminations in biomedical device CFRP plates. J. Electr. Eng. 2025, 76, 72–79.
6. Menniti, M.; Laganà, F.; Oliva, G.; Bianco, M.; Fiorillo, A.S.; Pullano, S.A. Development of Non-Invasive Ventilator for Homecare and Patient Monitoring System. Electronics 2024, 13, 790.
7. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1- 6). IEEE.
8. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
9. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
10. Li, Y.; Daho, M.E.H.; Conze, P.H.; Zeghlache, R.; Le Boité, H.; Tadayoni, R.; Cochener, B.; Lamard, M.; Quellec, G. A review of deep learning-based information fusion techniques for multimodal medical image classification. Comput. Biol. Med. 2024, 177, 108635.
11. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
12. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi- Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
13. Ruckert, J.; Bloch, L.; Brungel, R.; Idrissi-Yaghir, A.; Schäfer, H.; Schmidt, C.S.; Koitka, S.; Pelka, O.; Ben, A.; Abacha, A.G.; et al. ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset. Sci. Data 2024, 11, 688.
14. Li, Q.; Yang, Z.; Chen, K.; Zhao, M.; Long, H.; Deng, Y.; Hu, H.; Jia, C.; Wu, M.; Zhao, Z.; et al. Human-multimodal deep learning collaboration in ‘precise’diagnosis of lupus erythematosus subtypes and similar skin diseases. J. Eur. Acad. Dermatol. Venereol. 2024, 38, 2268–2279.
15. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
16. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
17. Reith, T.P.; D’Alessandro, D.M.; D’Alessandro, M.P. Capability of multimodal large language models to interpret pediatric radiological images. Pediatr. Radiol. 2024, 54, 1729–1737.
18. Martin, S.A.; Zhao, A.; Qu, J.; Imms, P.E.; Irimia, A.; Barkhof, F.; Cole, J.H.; Initiative, A.D.N. Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis. medRxiv 2025.
19. Mishra, S. R., & Dash, S. (2026). AI ‐ Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper ‐ Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
20. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211- 224). Cham: Springer Nature Switzerland.
21. Xue, C.; Kowshik, S.S.; Lteif, D.; Puducheri, S.; Jasodanand, V.H.;
22. Zhou, O.T.; Walia, A.S.; Guney, O.B.; Zhang, J.D.; Pham, S.T.; et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 2024, 30, 2977–2989.
23. Schilcher, J.; Nilsson, A.; Andlid, O.; Eklund, A. Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures. Comput. Biol. Med. 2024, 168, 107704.
24. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
25. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
26. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
27. Niu, S.; Ma, J.; Bai, L.; Wang, Z.; Guo, L.; Yang, X. EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation. Inf. Fusion 2024, 102, 102069.
28. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
29. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1- 8). IEEE.
30. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317 Zeng, L.; Ma, P.; Li, Z.; Liang, S.; Wu, C.; Hong, C.; Li, Y.; Cui, H.;
31. Li, R.; Wang, J.; et al. Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia. Adv. Sci. 2024, 11, 2404047.
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