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Cited by 3,146Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareArtificial Intelligence Applications

The DOI matches its Crossref record; 113 of the 114 references it cites were found in a registry or an index.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Open access
Alowais, Shuroug A., Alghamdi, Sahar S., Alsuhebany, Nada, Alqahtani, Tariq, Alshaya, Abdulrahman I., Almohareb, Sumaya N., Aldairem, Atheer, Alrashed, Mohammed, Bin Saleh, Khalid, Badreldin, Hisham A., Al Yami, Majed S., Al Harbi, Shmeylan, Albekairy, Abdulkareem M.·2023·BMC Medical Education
DOI 10.1186/s12909-023-04698-zPMC PMC10517477
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Abstract

Introduction: Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. Research Significance: This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI’s significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. Materials and Methods: The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. Results: Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. Conclusion: AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

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Crossref · 10.1186/s12909-023-04698-z

Cites

113 of 114 references verified0 citation errors · 1 not checkable

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Fig. 1 Tracing the Evolution of AI with a Better Understanding of the Relationship Between AI, ML, DL, and NLP
Fig. 1 Tracing the Evolution of AI with a Better Understanding of the Relationship Between AI, ML, DL, and NLPCC BY · PubMed Central
Fig. 2 Schematic representation of the process starting with the extraction of DNA/RNA, followed by sequencing. The subsequent genotypic alignment is performed using neural networks and deep learning. Probability calculations are achieved through applying statistical methods and M: The graph’s Y-axis denotes the probability (expressed in percentage) of a particular type of disease (hypertension, depression, breast cancer, and Alzheimer’s disease), while the X-axis signifies the count of gene mutations. Negative numbers indicate gene deletions, whereas positive values represent gene additions or nucleic acid mutations
Fig. 2 Schematic representation of the process starting with the extraction of DNA/RNA, followed by sequencing. The subsequent genotypic alignment is performed using neural networks and deep learning. Probability calculations are achieved through applying statistical methods and M: The graph’s Y-axis denotes the probability (expressed in percentage) of a particular type of disease (hypertension, depression, breast cancer, and Alzheimer’s disease), while the X-axis signifies the count of gene mutations. Negative numbers indicate gene deletions, whereas positive values represent gene additions or nucleic acid mutationsCC BY · PubMed Central
Fig. 3 Unlocking the Power of Patient Data with AI-Driven Predictive Analytics
Fig. 3 Unlocking the Power of Patient Data with AI-Driven Predictive AnalyticsCC BY · PubMed Central

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Citations

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2 retracted

Show all 113 references

Research works printed but not linked: 2

  1. Russell SJ. Artificial intelligence a modern approach. Pearson Education, Inc.; 2010.
  2. Ghosh PK, Jain P, Wankhede S, Preethi M, Kannan MK. Virtual nursing Assistant. J Geog Sci. 2021;8:279–85. 20.18001.GSJ.2021.V8I3.21.36690.

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Metadata

Authors
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M.
Affiliations
King Saud bin Abdulaziz University for Health Sciences (SA)
Corresponding author
Alowais, Shuroug A.
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2023-09-22
Volume
23
Issue
1
Pages
689-689
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-023-04698-z
PMC
PMC10517477
OpenAlex
W4386958277
Cited by
3,146
References
113
Citations by year
2026: 870 · 2025: 1525 · 2024: 721 · 2023: 27
FWCI
36.88
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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 practice, disease diagnosis, treatment guidelines, patient engagement, personalized medicine, clinical decision support, virtual health assistants, mental health services, patient education, physician-patient trust, data privacy, algorithmic bias, human expertise, AI adoption barriers
MeSH terms
Artificial Intelligence, Health Personnel, Humans, Income, Patient Participation, Quality of Life

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