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Cited by 107Artificial Intelligence in Healthcare and EducationAI in cancer detectionArtificial Intelligence in Healthcare

Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery

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Ogut, Eren·2025·Clinics and Practice
DOI 10.3390/clinpract15090169PMC PMC12468291
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Abstract

Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A comprehensive PubMed search was performed without language or publication date restrictions, combining Medical Subject Headings (MeSH) and free-text keywords for AI with domain-specific terms. The search yielded 2047 records, of which 243 duplicates were removed, leaving 1804 unique studies. After screening titles and abstracts, 1482 records were excluded due to irrelevance, preclinical scope, or lack of patient-level outcomes. Full-text review of 322 articles led to the exclusion of 172 studies (no clinical validation or outcomes, n = 64; methodological studies, n = 43; preclinical and in vitro-only, n = 39; conference abstracts without peer-reviewed full text, n = 26). Ultimately, 150 studies met inclusion criteria and were analyzed qualitatively. Data extraction focused on study context, AI technique, dataset characteristics, comparator benchmarks, and reported outcomes, such as diagnostic accuracy, area under the curve (AUC), efficiency, and clinical improvements. Results: AI demonstrated strong performance in diagnostic imaging, achieving expert-level accuracy in tasks such as cancer detection (AUC up to 0.94). CDSS showed promise in predicting adverse events (sepsis, atrial fibrillation), though real-world outcome evidence was mixed. In surgery, AI enhanced intraoperative guidance and risk stratification. Pathology benefited from AI-assisted diagnosis and molecular inference from histology. AI also accelerated drug discovery through protein structure prediction and virtual screening. However, challenges included limited explainability, data bias, lack of prospective trials, and regulatory hurdles. Conclusions: AI is transforming clinical medicine, offering improved accuracy, efficiency, and discovery. Yet, its integration into routine care demands rigorous validation, ethical oversight, and human-AI collaboration. Continued interdisciplinary efforts will be essential to translate these innovations into safe and effective patient-centered care.

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  • Clinical AI is increasingly moving beyond retrospective model development into prospective evaluation, implementation, and routine clinical care.

    “Clinical artificial intelligence (AI) is increasingly moving beyond retrospective model development into prospective evaluation, implementation, and routine clinical care [1,2].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [1]

    Results §3.2 says some large healthcare systems have started prospective trials of AI clinical decision support, supporting movement beyond retrospective evaluation. But Discussion §4.5 says most AI tools were evaluated retrospectively, and the Introduction and Conclusions describe integration into routine care as early-stage and ongoing. The claim overstates how far implementation and routine use have progressed. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Ogut, E.
Affiliations
Department of Anatomy, Faculty of Medicine, Istanbul Medeniyet University, Istanbul 34700, Türkiye
ORCID
Ogut, Eren
Venue
Clinics and Practice
Publisher
MDPI AG
Published
2025-09-16
Volume
15
Issue
9
Pages
169
ISSN
  • 2039-7275 (ISSN-L)
  • 2039-7283
DOI
10.3390/clinpract15090169
PMC
PMC12468291
OpenAlex
W4414267489
Cited by
107
References
47
Citations by year
2026: 85 · 2025: 22
FWCI
6.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinics and Practice (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Wellcome Trust (CC2025)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
diagnostic imaging, clinical decision support systems, AI-assisted surgery, AI-assisted pathology, drug discovery, cancer detection, adverse event prediction, sepsis prediction, atrial fibrillation prediction, intraoperative guidance, risk stratification, protein structure prediction, virtual screening, explainability, data bias, regulatory challenges, clinical validation, human-AI collaboration

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