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Cited by 329Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareArtificial Intelligence Applications

Artificial Intelligence in Healthcare and Medicine: Clinical Applications, Therapeutic Advances, and Future Perspectives

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Fahim, Yosri A., Hasani, Ibrahim W., Kabba, Samer, Ragab, Waleed Mahmoud·2025·European Journal of Medical Research
DOI 10.1186/s40001-025-03196-wPMC PMC12455834
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Abstract

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.

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  • Deep learning models can identify intricate patterns within large datasets without requiring explicit feature selection.

    “Unlike traditional rule-based approaches, deep learning models can identify intricate patterns within large datasets without requiring explicit feature selection [3].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [3]

    The Foundational AI technologies sections (Deep learning and Machine learning; pages unavailable) say that deep learning represents complex, high-dimensional data and enhances feature extraction, while machine learning recognizes patterns in extensive, intricate datasets. They do not establish that deep learning requires no explicit feature selection or make the claimed comparison with traditional rule-based approaches. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M.
Affiliations
  • Galala University (EG)
  • Al Andalus University (SY)
Corresponding author
Fahim, Yosri A.
ORCID
Venue
European Journal of Medical Research
Publisher
Springer Science and Business Media LLC
Published
2025-09-23
Volume
30
Issue
1
Pages
848-848
ISSN
  • 0949-2321 (ISSN-L)
  • 2047-783X
DOI
10.1186/s40001-025-03196-w
PMC
PMC12455834
OpenAlex
W4414413410
Cited by
329
References
20
Citations by year
2026: 317 · 2025: 12
FWCI
19.45
Type
journal article
Language
en
Open access
Licence
Locations
  • European journal of medical research (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
Galala 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 › Artificial Intelligence Applications
Keywords
artificial intelligence in healthcare, clinical applications of AI, medical diagnostics, personalized medicine, drug discovery, predictive analytics, telemedicine, wearable health technology, mobile diagnostics, resource-limited settings, data privacy, algorithmic bias, model interpretability, AI regulation, electronic health records, diagnostic imaging, genomic data
MeSH terms
Artificial Intelligence, Delivery of Health Care, Humans, Telemedicine, Precision Medicine

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