Artificial Intelligence in Healthcare Practice: Validation, Fairness, and Regulatory Challenges: A Systematic Review
TL;DR
AI demonstrates strong potential to improve the effectiveness, safety, and quality of healthcare, however, broader clinical adoption remains constrained by regulatory requirements, interpretability gaps, data quality issues, and workflow integration challenges, underscoring the need for stronger validation practices and more implementation-focused research.
Abstract
Introduction Artificial intelligence (AI) is reshaping healthcare, enabled by advances in computing, affordable data storage, and the widespread adoption of electronic health records (EHRs). Machine learning (ML), deep learning (DL), and natural language processing (NLP) are increasingly used for disease diagnosis, risk prediction, and treatment planning. Objective This systematic review aimed to examine AI applications across clinical domains from 2020 to 2025, assess their diagnostic accuracy and clinical performance relative to standard practice, identify key implementation barriers including regulatory compliance, algorithmic fairness, and transparency challenges, and compare validation practices and methodological quality with earlier systematic reviews. Methods This systematic review followed PRISMA 2020 guidelines. We searched five databases (PubMed, IEEE Xplore, Web of Science, Springer, and Semantic Scholar) for studies published from January 2020 to September 2025. We included original clinical AI studies that reported prospective validation and/or external validation. Results Twenty studies met the inclusion criteria. Publication volume peaked in 2024 (n = 7, 35.0%). DL approaches were most common (n = 12, 60.0%), with convolutional neural networks (CNNs) frequently applied to medical imaging tasks. By clinical domain, 30.0% of studies focused on radiology (n = 6), 20.0% on oncology (n = 4), and 15.0% on cardiology (n = 3). For imaging-based diagnostic models, the descriptive median performance across individual studies was 0.91 AUC (no formal meta-analysis was conducted due to heterogeneity in study designs, populations, and outcome metrics). The most frequently reported challenges were regulatory compliance (55.0%, n = 11), limited algorithmic transparency (40.0%, n = 8), data quality limitations (35.0%, n = 7), and barriers to clinical integration (30.0%, n = 6). Conclusions AI demonstrates strong potential to improve the effectiveness, safety, and quality of healthcare. However, broader clinical adoption remains constrained by regulatory requirements, interpretability gaps, data quality issues, and workflow integration challenges, underscoring the need for stronger validation practices and more implementation-focused research.