The Role of Artificial Intelligence in Early Clinical Diagnosis: A Systematic Literature Review of Current Challenges and Future Directions

Abstract

Background: Diagnostic error remains a leading and preventable source of patient harm, and delayed detection of cancer, infection, cardiovascular disease and sensory disorders drives much avoidable morbidity. Artificial intelligence (AI), including deep learning, machine learning and, more recently, large language models, is increasingly proposed as a means of detecting disease earlier and more reliably.

Objective: To synthesise the evidence on AI in early clinical diagnosis, to appraise the secondary literature that has already reviewed the field, and to identify the technical, clinical, ethical, regulatory, health-system and equity challenges that separate laboratory performance from clinical benefit.

Methods: A structured, PRISMA 2020-guided review was conducted. The review covered primary diagnostic accuracy studies, prospective evaluations, randomised trials, systematic reviews, meta-analyses, regulatory analyses and policy documents. Methodological quality was interpreted through the QUADAS-2, QUADAS-AI and PROBAST frameworks. Findings were organised by clinical domain and by analytical perspective.

Results: In retrospective, image-based tasks (retinal, dermatological, mammographic, thoracic, histopathological and electrocardiographic), AI systems frequently match or exceed clinicians. Evidence of real-world clinical benefit is much thinner. Related reviews repeatedly report high heterogeneity, high risk of bias, scarce external validation and few prospective or randomised studies. Performance degrades across sites, devices and populations. Documented algorithmic bias, automation bias, limited explainability and unresolved regulatory and liability questions further constrain adoption. Evidence from low- and middle-income settings, including India, suggests that infrastructure, data governance and workforce readiness shape the benefit of AI as much as algorithmic accuracy does.

Conclusion: The central barrier is the translational gap between statistical accuracy and demonstrated patient benefit. Closing it requires prospective multi-site validation, standardised reporting, subgroup-level fairness auditing, human-factors-informed deployment, federated and privacy-preserving data collaboration, and governance frameworks designed for adaptive software.