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Cited by 82Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsArtificial Intelligence in Healthcare

Redefining Healthcare With Artificial Intelligence (AI): The Contributions of ChatGPT, Gemini, and Co-pilot

Open access
Alhur, Anas·2024·Cureus
DOI 10.7759/cureus.57795PMC PMC11077095
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Abstract

Artificial Intelligence (AI) in healthcare marks a new era of innovation and efficiency, characterized by the emergence of sophisticated language models such as ChatGPT (OpenAI, San Francisco, CA, USA), Gemini Advanced (Google LLC, Mountain View, CA, USA), and Co-pilot (Microsoft Corp, Redmond, WA, USA). This review explores the transformative impact of these AI technologies on various facets of healthcare, from enhancing patient care and treatment protocols to revolutionizing medical research and tackling intricate health science challenges. ChatGPT, with its advanced natural language processing capabilities, leads the way in providing personalized mental health support and improving chronic condition management. Gemini Advanced extends the boundary of AI in healthcare through data analytics, facilitating early disease detection and supporting medical decision-making. Co-pilot, by integrating seamlessly with healthcare systems, optimizes clinical workflows and encourages a culture of innovation among healthcare professionals. Additionally, the review highlights the significant contributions of AI in accelerating medical research, particularly in genomics and drug discovery, thus paving the path for personalized medicine and more effective treatments. The pivotal role of AI in epidemiology, especially in managing infectious diseases such as COVID-19, is also emphasized, demonstrating its value in enhancing public health strategies. However, the integration of AI technologies in healthcare comes with challenges. Concerns about data privacy, security, and the need for comprehensive cybersecurity measures are discussed, along with the importance of regulatory compliance and transparent consent management to uphold ethical standards and patient autonomy. The review points out the necessity for seamless integration, interoperability, and the maintenance of AI systems' reliability and accuracy to fully leverage AI's potential in advancing healthcare.

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  • Remaining confounding could not be fully managed due to past digital literacy, learning styles, and educator proficiency.

    “Remaining confounding could not be fully managed, as a result of past digital literacy, learning styles and educator proficiency [29,30].”

    From HOW AI TOOLS (LIKE CHATGPT/GEMINI) AFFECT CRITICAL THINKING IN NURSING STUDENTS. A SYSTEMATIC REVIEW (2026) · cites this paper as [29]

    The Review discusses workforce training and algorithmic bias, but not residual confounding from prior digital literacy, learning styles, or educator proficiency. Searches for those terms and for residual confounding found no passage addressing the claim; the closest education-related matches were titles in the References section (page unavailable). (LLM, conf=0.76)

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Research works printed but not linked: 25

  1. [2]Screening/diagnosis of pediatric endocrine disorders through the artificial intelligence model in different language settings Eur J Pediatr Ying L Li S Chen C 2024 PMC11098926 38502320
  2. [3]Delving into educators’ perspectives on ChatGPT in management education: a qualitative exploration [PREPRINT] Interact Technol Smart Educ Bhaskar P Gupta PK 2024
  3. [5]Charting new AI education in gastroenterology: cross-sectional evaluation of ChatGPT and perplexity AI in medical residency exam [PREPRINT] Digest Liver Dis Gravina AG Pellegrino R Palladino G Imperio G Ventura A Federico A 2024 38503659
  4. [6]Artificial intelligence in blood transcriptomics In: Artificial Intelligence in Medicine Warnat-Herresthal S Oestreich M Schultze JL Becker M 1109 1123 Cham Springer International Publishing 2022 https://link.springer.com/referenceworkentry/10.1007/978-3-030-64573-1_262 · DOI
  5. [7]Digitally assisted mindfulness in training self-regulation skills for sustainable mental health: a systematic review Behav Sci (Basel) Mitsea E Drigas A Skianis C 1008 13 2023 38131865 PMC10740653
  6. [8]Big medical data, cloud computing, and artificial intelligence for improving diagnosis in healthcare In: Big Data Analytics for Healthcare Kazancigil MA 139 150 Academic Press 2022
  7. [9]Deep learning-enabled medical computer vision NPJ Digit Med Esteva A Chou K Yeung S 5 4 2021 33420381 PMC7794558
  8. [10]Artificial intelligence and machine learning technology driven modern drug discovery and development Int J Mol Sci Sarkar C Das B Rawat VS 2026 24 2023 36768346 PMC9916967
  9. [11]Faculty development initiatives designed to promote leadership in medical education. A BEME systematic review: BEME guide no. 19 Medical Teacher Steinert Y Naismith L Mann K 483 503 34 2012 22578043
  10. [13]Predicting the future - Big data, machine learning, and clinical medicine N Engl J Med Obermeyer Z Emanuel EJ 1216 1219 375 2016 27682033 PMC5070532
  11. [15]Why digital medicine depends on interoperability NPJ Digit Med Lehne M Sass J Essenwanger A Schepers J Thun S 79 2 2019 31453374 PMC6702215
  12. [16]Algorithmic fairness in artificial intelligence for medicine and healthcare Nat Biomed Eng Chen RJ Wang JJ Williamson DF 719 742 7 2023 https://www.nature.com/articles/s41551-023-01056-8 37380750 PMC10632090
  13. [17]Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg Naik N Hameed BM Shetty DK 862322 9 2022 35360424 PMC8963864
  14. [19]Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health Wahl B Cossy-Gantner A Germann S Schwalbe NR 0 3 2018 PMC6135465 30233828
  15. [20]High-performance medicine: the convergence of human and artificial intelligence Nat Med Topol EJ 44 56 25 2019 https://www.nature.com/articles/s41591-018-0300-7 30617339
  16. [21]Gender shades: intersectional accuracy disparities in commercial gender classification PMLR Buolamwini J Gebru T 1 15 81 2018 http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
  17. [24]Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA Gulshan V Peng L Coram M 2402 2410 316 2016 27898976
  18. [25]Delivering personalized medicine in retinal care: from artificial intelligence algorithms to clinical application Curr Opin Ophthalmol Jill Hopkins J Keane PA Balaskas K 329 336 31 2020 32740060
  19. [26]Application of deep learning methods in biological networks Brief Bioinform Jin S Zeng X Xia F Huang W Liu X 1902 1917 22 2021 32363401
  20. [27]Risk of adverse outcomes in hospitalized patients with autoimmune disease and COVID-19: a matched cohort study from New York City J Rheumatol Faye AS Lee KE Laszkowska M 454 462 48 2021 33132221 PMC8087816
  21. [30]A patient-centric healthcare framework reference architecture for better semantic interoperability based on Blockchain, Cloud, and IoT IEEE Access Gohar AN Abdelmawgoud SA Farhan MS 92137 92157 10 2022
  22. [31]Recent advancements in emerging technologies for healthcare management systems: a survey Healthcare (Basel) Junaid SB Imam AA Balogun AO 1940 10 2022 36292387 PMC9601636
  23. [33]Exploring Saudi Arabia individuals' attitudes toward electronic personal health records J Comput Sci Technol Studies Alhur A 80 87 4 2022 https://www.researchgate.net/publication/361221676_Exploring_Saudi_Arabia_Individuals'_Attitudes_toward_Electronic_Personal_Health_Records
  24. [37]Telemental health and artificial intelligence: knowledge and attitudes of Saudi Arabian individuals towards AI-integrated telemental health JPTCP Alhur AA Alhur AA Aldhafeeri MD 1993 2009 30 2023 https://www.researchgate.net/publication/374699939_TELEMENTAL_HEALTH_AND_ARTIFICIAL_INTELLIGENCE_KNOWLEDGE_AND_ATTITUDES_OF_SAUDI_ARABIAN_INDIVIDUALS_TOWARDS_AI-INTEGRATED_TELEMENTAL_HEALTH
  25. [38]Evaluating computer science students' experiences and motivation towards learning artificial intelligence BJTEP Alhur A Alhur A Alhur AA 49 56 2 2023 https://www.researchgate.net/publication/376090909_Evaluating_Computer_Science_Students'_Experiences_and_Motivation_Towards_Learning_Artificial_Intelligence

Cited by

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Authors
Alhur, A.
Affiliations
University of Ha'il (SA)
Corresponding author
Alhur, Anas
ORCID
Alhur, Anas
Venue
Cureus
Publisher
Springer Science and Business Media LLC
Published
2024-04-07
Volume
16
Issue
4
Pages
e57795-e57795
ISSN
2168-8184 (ISSN-L)
DOI
10.7759/cureus.57795
PMC
PMC11077095
OpenAlex
W4394573140
Cited by
82
References
3
Citations by year
2026: 18 · 2025: 47 · 2024: 16 · 2022: 1
FWCI
1.85
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Cureus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence in healthcare, ChatGPT, Gemini, Microsoft Copilot, large language models, natural language processing, chronic disease management, early disease detection, clinical decision-making, clinical workflow optimization, drug discovery, genomics, personalized medicine, COVID-19 pandemic, data privacy, cybersecurity, regulatory compliance, patient consent, interoperability, AI reliability

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