Applications of AI in Healthcare for Disease Diagnosis and Treatment

Abstract

Magnetic Resonance Imaging (MRI) plays a pivotal role in the early diagnosis and management of tumors. Artificial intelligence (AI) has emerged as a transformative tool for automating tumor detection, enhancing diagnostic accuracy, and supporting treatment decision-making. This study proposes a unified AI framework integrating MRI-based tumor classification with treatment policy optimization for chemotherapy and radiotherapy planning. The methodology involves three stages: (1) Data preprocessing with statistical imputation, normalization, and dimensionality reduction, (2) Diagnosis modeling using a convolutional neural network (CNN) for tumor segmentation and classification, and (3) Treatment recommendation through causal inference and reinforcement learning to optimize therapy strategies. Mathematically, the diagnosis model minimizes a cross-entropy loss over segmented MRI data, while the treatment policy is formalized as a Markov Decision Process (MDP) maximizing patient outcome rewards. The causal inference module estimates average treatment effect (ATE) using inverse probability weighting and doubly robust estimators to ensure unbiased treatment recommendations. Experiments will be conducted on publicly available MRI tumor datasets (e.g., BraTS Challenge), evaluating diagnostic performance with sensitivity, specificity, and AUC, while treatment recommendation quality is measured via survival gain and policy value. The proposed framework provides a dual benefit: accurate tumor diagnosis and personalized treatment planning. By combining deep learning, statistical preprocessing, and causal reinforcement learning, this research aims to bridge the gap between diagnostic AI and decision-support systems in oncology. The study underscores the importance of interpretable, fair, and privacy-preserving AI models for clinical adoption in tumor management.