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Cited by 201Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareMachine Learning in Healthcare

The DOI matches its Crossref record; 123 of the 124 references it cites were found in a registry or an index.

Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions

Open access
Khalifa, Mohamed, Albadawy, Mona·2024·Computer Methods and Programs in Biomedicine Update
DOI 10.1016/j.cmpbup.2024.100148
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Abstract

Clinical prediction is integral to modern healthcare, leveraging current and historical medical data to forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, and personalised care leading to better patient outcomes and healthcare efficiency. This systematic review implemented a structured four-step methodology, including an extensive literature search in academic databases (PubMed, Embase, Google Scholar), applying specific inclusion and exclusion criteria, data extraction focusing on AI techniques and their applications in clinical prediction, and a thorough analysis of the collected information to understand AI's roles in enhancing clinical prediction. Through the analysis of 74 experimental studies, eight key domains, where AI significantly enhances clinical prediction, were identified: 1) Diagnosis and early detection of disease; 2) Prognosis of disease course and outcomes; 3) Risk assessment of future disease; 4) Treatment response for personalised medicine; 5) Disease progression; 6) Readmission risks; 7) Complication risks; and 8) Mortality prediction. Oncology and radiology come on top of the specialties benefiting from AI in clinical prediction. The review highlights AI's transformative impact across various clinical prediction domains, including its role in revolutionising diagnostics, improving prognosis accuracy, aiding in personalised medicine, and enhancing patient safety. AI-driven tools contribute significantly to the efficiency and effectiveness of healthcare delivery. AI's integration in clinical prediction marks a substantial advancement in healthcare. Recommendations include enhancing data quality and accessibility, promoting interdisciplinary collaboration, focusing on ethical AI practices, investing in AI education, expanding clinical trials, developing regulatory oversight, involving patients in the AI integration process, and continuous monitoring and improvement of AI systems.

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Crossref · 10.1016/j.cmpbup.2024.100148

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Metadata

Authors
Khalifa, M., & Albadawy, M.
Affiliations
  • La Trobe University (AU)
  • UNSW Sydney (AU)
Corresponding author
  • Khalifa, Mohamed
  • Albadawy, Mona
ORCID
Khalifa, Mohamed
Venue
Computer Methods and Programs in Biomedicine Update
Publisher
Elsevier BV
Published
2024
Volume
5
Pages
100148
ISSN
2666-9900 (ISSN-L)
DOI
10.1016/j.cmpbup.2024.100148
OpenAlex
W4392560365
Cited by
201
References
124
Citations by year
2026: 69 · 2025: 110 · 2024: 22
FWCI
18.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer Methods and Programs in Biomedicine Update (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by-nc-nd)
  • La Trobe University
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
artificial intelligence, clinical prediction, disease diagnosis, early detection, prognosis, risk assessment, personalized medicine, treatment response, disease progression, hospital readmission, complication prediction, mortality prediction, radiology, patient safety, ethical AI, AI regulation

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