Open Access

REVIEW

A Narrative Review of Artificial Intelligence in Medical Diagnostics

Takanobu Hirosawa^*, Taro Shimizu

Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, 321-0293, Japan

* Corresponding Author: Takanobu Hirosawa. Email:

(This article belongs to the Special Issue: Generative AI for Recommendation Services)

Computers, Materials & Continua 2025, 83(3), 3919-3944. https://doi.org/10.32604/cmc.2025.063803

Received 24 January 2025; Accepted 16 April 2025; Issue published 19 May 2025

Abstract

Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine learning and deep learning algorithms to analyze vast datasets, identify patterns, and generate accurate differential diagnoses. AI’s potential in diagnostics is demonstrated through applications in genomics, predictive analytics, and early disease detection, with successful case studies in oncology, radiology, pathology, ophthalmology, dermatology, gastroenterology, and psychiatry. These applications demonstrate AI’s ability to process complex medical data, facilitate early intervention, and extend specialized care to underserved populations. However, integrating AI into diagnostics faces significant limitations, including technical challenges related to data quality and system integration, regulatory hurdles, ethical concerns about transparency and bias, and risks of misinformation and overreliance. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and continuous advancements in AI technology. The future of AI in diagnostics promises further innovations in multimodal AI, genomic data integration, and expanding access to high-quality diagnostic services globally. Responsible and ethical implementation of AI will be crucial to fully realize its potential, ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes. This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics, advocating for sustained investment and collaborative efforts to harness its benefits effectively.

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