Role of Artificial Intelligence in Early Disease Diagnosis: A Narrative Review

Abstract

Background: The integration of artificial intelligence (AI) into healthcare has opened new frontiers in the early detection and diagnosis of complex diseases. AI-powered systems, including machine learning (ML) and deep learning (DL) algorithms, are demonstrating remarkable performance in analysing medical images, electronic health records (EHR), and multi-modal clinical data. Early disease diagnosis is critical as it improves prognosis, reduces treatment costs, and lowers disease burden.

Objective: This narrative review critically examines the current role of AI in early disease diagnosis, focusing on applications in medical imaging, predictive modelling, clinical decision support systems (CDSS), and disease-specific screening including cancer, diabetes, and cardiovascular diseases.

Methods: A comprehensive review of literature published between 2018 and 2025 was conducted using PubMed, Google Scholar, IEEE Xplore, and Scopus databases. Studies involving AI-based diagnostic tools, validated against clinical benchmarks, were included.

Results: Evidence from multiple peer-reviewed studies demonstrates that AI models achieve diagnostic accuracy comparable to or exceeding that of experienced clinicians across multiple disease domains [1, 2]. Deep convolutional neural networks (CNNs) have shown particular promise in radiology and pathology, while recurrent architectures excel at temporal EHR analysis [3].

Conclusion: AI holds transformative potential for early disease diagnosis. However, widespread clinical adoption is contingent upon addressing challenges related to data quality, model interpretability, regulatory oversight, and equitable access. Future research should focus on multimodal integration and prospective clinical validation.