GenAI Agents for Early Disease Diagnosis: A Review of Architectures, Applications, and Policy Directions

The integration of Artificial Intelligence (AI) agents into healthcare represents a paradigm shift in medical diagnostics, enabling autonomous systems that leverage multimodal data fusion, advanced machine learning architectures, and clinical reasoning engines. We explore their architectural components, including perception, knowledge base, reasoning engine, and decision-making modules. The paper then delves into key application areas such as medical imaging analysis, rare disease identification, multimodal diagnostic dialogue, and AI-powered generalist diagnostic agents. We examine the core architectural components including perception modules for EHR integration (HL7/FHIR standards), medical imaging analysis (DICOM, CNN architectures), genomic data processing (FASTQ/BAM formats), and multimodal biomarker integration. The paper details specialized AI agents for medical imaging analysis using 2D/3D convolutional neural networks and vision transformers, rare disease diagnosis through few-shot learning and knowledge graph reasoning, and multimodal diagnostic systems exemplified by Google's AMIE framework. We evaluate the technical implementation challenges including data privacy compliance (HIPAA, GDPR), model interpretability requirements (SHAP, LIME explanations), and regulatory considerations (FDA SaMD frameworks). Performance analysis demonstrates significant improvements in diagnostic accuracy (AUC-ROC improvements of 15-25\% across studies), operational efficiency through automated workflow orchestration, and early disease detection capabilities surpassing traditional diagnostic methods. The synthesis of recent publications indicates that AI diagnostic agents achieve clinical performance comparable to healthcare professionals in specific domains while enabling proactive healthcare through predictive analytics and personalized treatment recommendations. Furthermore, we analyze the significant benefits offered by these systems, including improved diagnostic precision, operational efficiency, and personalized patient care. Finally, we address the critical challenges and future research directions, focusing on data privacy, model interpretability, regulatory hurdles, and the path toward medical superintelligence. Future research directions focus on federated learning approaches for privacy-preserving model training, explainable AI for clinical trust adoption, and the development of medical superintelligence systems capable of holistic patient health modeling across temporal and multimodal data dimensions.