Explainable and Personalized AI Models for Real-Time Diagnostic Accuracy and Treatment Optimization in Healthcare

Jagadeesan N., V. Subba Ramaiah, S. Indhumathi, Hari Shankar Punna, K. Jamberi, Amitabha Mandal

2025

Abstract

Healthcare is being transformed using Artificial Intelligence (AI) through the development of treatment protocols that incorporate data analysis, personalized medicine, and preventive healthcare. This study provides an understandable and real-time carrier of AI improving diagnostic accuracy and personalized medical decisions. In contrast to previous black-box models, our model integrates SHAP- and LIME-based interpretability, thus guaranteeing clinical transparency and trust. We tackle data bias by fairness-aware modeling (on fair representation learning and empirically studying the impact of fair models) and by having varied, multi-modal data in order to increase generalization across populations. The integration with electronic health records (EHRs) enables easy-to-deploy solutions in real clinical settings, and the learning models customise the treatment according to the patients' information, including the patient profile, genetic information and the patient's life style. Extensive validation, including regulatory-compliant benchmarks, shows the system’s real-world readiness and better performance compared to state-of-the-art models. The presented solution closes the loop between advanced machine learning and clinical utility, providing a scalable, ethically grounded, and patient focus AI system for the healthcare of the future.

Paper Citation

in Harvard Style

N. J., Ramaiah V., Indhumathi S., Punna H., Jamberi K. and Mandal A. (2025). Explainable and Personalized AI Models for Real-Time Diagnostic Accuracy and Treatment Optimization in Healthcare. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 493-499. DOI: 10.5220/0013868100004919

in Bibtex Style

@conference{icrdicct`2525,
author={Jagadeesan N. and V. Ramaiah and S. Indhumathi and Hari Punna and K. Jamberi and Amitabha Mandal},
title={Explainable and Personalized AI Models for Real-Time Diagnostic Accuracy and Treatment Optimization in Healthcare},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={493-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013868100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Explainable and Personalized AI Models for Real-Time Diagnostic Accuracy and Treatment Optimization in Healthcare
SN - 978-989-758-777-1
AU - N. J.
AU - Ramaiah V.
AU - Indhumathi S.
AU - Punna H.
AU - Jamberi K.
AU - Mandal A.
PY - 2025
SP - 493
EP - 499
DO - 10.5220/0013868100004919
PB - SciTePress