Pioneering AI Solutions for Cancer Subtype Classification through Gene Expression

Abstract

Improved diagnostic models for personalized Cancer profiling are required significantly, utilizing AI methods to enhance accuracy, support early detection, and inform targeted treatment strategies. Despite significant progress in cancer prediction, current approaches often struggle with issues of generalizability across diverse patient cohorts, computational inefficiencies, and managing heterogeneous data sources. This paper delves into the fast developing topic of AI-driven tumor class categorization utilizing expression of genes data. Focusing on machine learning (ML), explainable artificial intelligence (XAI), neural network, and transfer learning techniques. The integration of innovative AI methodologies is crucial for understanding complex genetic interactions, improving model interpretability through XAI, and enabling adaptive learning through transfer learning. This will allow medical practitioners to rely on AI-driven insights and provide strong, scalable solutions for everyday life applications in medicine. The analysis recognizes existing limitations, including the absence of established methods on cross-institutional sharing of information and the difficulties in maintaining model adaptation to different tumor subtypes. This work underscores the potential of AI to revolutionize cancer subtype classification, fostering advancements that could reshape personalized oncology, improve patient outcomes, and establish a new standard for precision medicine. Unlike prior reviews, this study goes beyond summarizing methods by synthesizing cross-cutting gaps across ML, neural network (NN), XAI, and transfer learning (TL) approaches. It further proposes a conceptual framework that integrates these methodologies to guide future research in developing clinically deployable and patient-centered cancer diagnostic systems.