The impact of artificial intelligence in medicine on the future role of the physician
Open accessAbstract
The practice of medicine is changing with the development of new Artificial Intelligence (AI) methods of machine learning. Coupled with rapid improvements in computer processing, these AI-based systems are already improving the accuracy and efficiency of diagnosis and treatment across various specializations. The increasing focus of AI in radiology has led to some experts suggesting that someday AI may even replace radiologists. These suggestions raise the question of whether AI-based systems will eventually replace physicians in some specializations or will augment the role of physicians without actually replacing them. To assess the impact on physicians this research seeks to better understand this technology and how it is transforming medicine. To that end this paper researches the role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology. It concludes that AI-based systems will augment physicians and are unlikely to replace the traditional physician–patient relationship.
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Cites
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775 · see citing works
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174944 · Cited by 1,346 · see citing works
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129 · see citing works
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965 · see citing works
Artificial intelligence in retina
Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779 · see citing works
The coming of age of artificial intelligence in medicine
Patel, V. L., Shortliffe, E. H., Mario Stefanelli, A., Peter Szolovits, B., Berthold, M. R., Bellazzi, R., & Abu–Hanna, A. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.07.017 · Cited by 629 · see citing works
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning
Schlegl, T., Waldstein, S. M., Bogunović, H., Endstraßer, F., Sadeghipour, A., Philip, A.-M., Podkowinski, D., Gerendas, B. S., Langs, G., & Schmidt-Erfurth, U. M. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.10.031 · Cited by 601 · see citing works
Somashekhar, S. P., Sepúlveda, M.-J., Puglielli, S. M., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., Rauthan, A., Kumar, N., Patil, P., Rhee, K. B. L., et al. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx781 · Cited by 366 · see citing works
Development of machine learning models for diagnosis of glaucoma
Kim, S.-J., Cho, K. J., & Oh, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177726 · Cited by 267 · see citing works
The future of radiology augmented with Artificial Intelligence: A strategy for success
Liew, C. J. Y. (2018) · European Journal of Radiology · doi:10.1016/j.ejrad.2018.03.019 · Cited by 258 · see citing works
Dawes, T. J. W., de Marvao, A., Shi, W., Fletcher, T., Watson, G. M. J., Wharton, J., Rhodes, C. J., Howard, L., Gibbs, J. S. R., Rueckert, D., et al. (2017) · Radiology · doi:10.1148/radiol.2016161315 · Cited by 234 · see citing works
The rise of artificial intelligence and the uncertain future for physicians
Krittanawong, C. (2017) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2017.06.017 · Cited by 205 · see citing works
Artificial Intelligence: Threat or Boon to Radiologists?
Recht, M., & Bryan, R. N. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.07.007 · Cited by 172 · see citing works
Na, L., Yang, C., Lo, C.-C., Zhao, F., Fukuoka, Y., & Aswani, A. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.6040 · Cited by 161 · see citing works
Administration, U. S. F. &. D. (2018) · Case Medical Research · doi:10.31525/fda2-ucm604357.htm · Cited by 146 · see citing works
Wottawa, C. R., Genovese, B., Nowroozi, B., et al. (2015) · Surgical Endoscopy · doi:10.1007/s00464-015-4602-2 · Cited by 121 · see citing works
Liu, X., Jiang, J., Zhang, K., Long, E., Cui, J., Zhu, M., An, Y., Zhang, J., Liu, Z., Lin, Z., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0168606 · Cited by 113 · see citing works
Artificial intelligence for breast cancer screening: Opportunity or hype?
Houssami, N., Lee, C. I., Buist, D. S. M., & Tao, D. (2017) · The Breast · doi:10.1016/j.breast.2017.09.003 · Cited by 106 · see citing works
Will Machine Learning Tip the Balance in Breast Cancer Screening?
Trister, A. D., Buist, D. S. M., & Lee, C. I. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.0473 · Cited by 92 · see citing works
What Is Artificial Intelligence?
Dignum, V. (2019) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-030-30371-6_2 · Cited by 81 · see citing works
Hidalgo, I. R., Rozema, J., Saad, A., et al. (2017) · Cornea · doi:10.1097/ico.0000000000001194 · Cited by 66 · see citing works
Artificial Intelligence and Radiology: What Will the Future Hold?
King, B. F. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.11.017 · Cited by 62 · see citing works
Artificial intelligence in primary care
Mistry, P. (2019) · British Journal of General Practice · doi:10.3399/bjgp19x705137 · Cited by 57 · see citing works
Kruskal, J. B., Berkowitz, S. J., Geis, J. R., et al. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.02.019 · Cited by 54 · see citing works
Poole, D., & Mackworth, A. K. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009258227 · Cited by 41 · see citing works
Understanding the advent of artificial intelligence in ophthalmology
Ahuja, A. S., & Halperin, L. S. (2019) · Journal of Current Ophthalmology · doi:10.1016/j.joco.2019.05.001 · Cited by 24 · see citing works
Self-citationDeep Learning in Radiology: Now the Real Work Begins
Lugo-Fagundo, C., Vogelstein, B., Yuille, A., & Fishman, E. K. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.007 · Cited by 22 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Frost & Sullivan 2016 From $600 M to $6 billion, artificial intelligence systems poised for dramatic market expansion in healthcare https://ww2.frost.com/news/press-releases/600-m-6-billion-artificial-intelligence-systems-poised-dramatic-market-expansion-healthcare/
- IBM 2015 Manipal hospitals adopts watson for oncology to help physicians identify options for individualized, evidence-based cancer care across India. Press release https://www.prnewswire.com/news-releases/manipal-hospitals-adopts-watson-for-oncology-to-help-physicians-identify-options-for-individualized-evidence-based-cancer-care-across-india-300186824.html December 2015
- Pearson D 2017 Artificial intelligence in radiology: the game-changer on everyone’s mind Radiology business https://www.radiologybusiness.com/topics/technology-management/artificial-intelligence-radiology-game-changer-everyones-mind
- Singh S 2018 Artificial Intelligence and the internet of things in healthcare The Journal of Mhealth https://thejournalofmhealth.com/artificial-intelligence-and-the-internet-of-things-in-healthcare/ April 2018
Cited by
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
“…in AI have impacted clinical pharmacology in many ways like literature searching and processing, interactions with online predictive ML models, ML methods in framing policy to encourage healthcare in many countries and also to get predictive analysis for drug-related information [449, 450].”
MethodSources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
Celi, L. A., Cellini, J., Charpignon, M.-L., Dee, E. C., Dernoncourt, F., Eber, R., Mitchell, W. G., Moukheiber, L., Schirmer, J., Situ, J., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000022 · Cited by 576 · see citing works
Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis
Choudhury, A., & Shamszare, H. (2023) · Journal of Medical Internet Research · doi:10.2196/47184 · Cited by 470 · see citing works
Artificial intelligence for good health: a scoping review of the ethics literature.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435 · see citing works
Artificial intelligence and learning analytics in teacher education: A systematic review
Salas-Pilco, S., Xiao, K., & Hu, X. (2022) · Education Sciences · doi:10.3390/educsci12080569 · Cited by 326 · see citing works
Blockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281 · see citing works
Ahuja, A. S., Polascik, B. W., Doddapaneni, D., Byrnes, E. S., & Sridhar, J. (2023) · Integrative Medicine Research · doi:10.1016/j.imr.2022.100917 · Cited by 256 · see citing works
Self-citationLeveraging ChatGPT for Enhancing Critical Thinking Skills
Guo, Y., & Lee, D. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00505 · Cited by 250 · see citing works
Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100148 · Cited by 201 · see citing works
Yelne, S., Chaudhary, M., Dod, K., Sayyad, A., & Sharma, R. (2023) · Cureus · doi:10.7759/cureus.49252 · Cited by 190 · see citing works
Bickman, L. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01065-8 · Cited by 184 · see citing works
ChatGPT in physics education: A pilot study on easy-to-implement activities
Bitzenbauer, P. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13176 · Cited by 181 · see citing works
Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024) · JMIR Medical Education · doi:10.2196/54793 · Cited by 165 · see citing works
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works
Designing Futuristic Telemedicine Using Artificial Intelligence and Robotics in the COVID-19 Era
Bhaskar, S., Bradley, S., Sakhamuri, S., Moguilner, S., Chattu, V. K., Pandya, S., Schroeder, S., Ray, D., & Banach, M. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.556789 · Cited by 137 · see citing works
A bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126 · see citing works
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Tudor Car, L., Kyaw, B. M., Nannan Panday, R. S., van der Kleij, R., Chavannes, N., Majeed, A., & Car, J. (2021) · JMIR Medical Education · doi:10.2196/28275 · Cited by 106 · see citing works
Kernberg, A., Gold, J. A., & Mohan, V. (2024) · Journal of Medical Internet Research · doi:10.2196/54419 · Cited by 120 · see citing works
“With the rapid and exponential growth in computing capacity, artificial intelligence (AI) is being increasingly used in health care, holding the promise of revolutionizing medical documentation, thus potentially alleviating the burden on physicians [6].”
Allen, M. R., Webb, S., Mandvi, A., Frieden, M., Tai-Seale, M., & Kallenberg, G. (2024) · BMC Primary Care · doi:10.1186/s12875-024-02282-y · Cited by 100 · see citing works
Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.755644 · Cited by 87 · see citing works
Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education
Weidener, L., & Fischer, M. (2024) · JMIR Medical Education · doi:10.2196/55368 · Cited by 90 · see citing works
Ergin, E., Karaarslan, D., Şahan, S., & Çınar Yücel, Ş. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13646 · Cited by 70 · see citing works
Laupichler, M. C., Hadizadeh, D. R., Wintergerst, M. W. M., von der Emde, L., Paech, D., Dick, E. A., & Raupach, T. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03866-x · Cited by 62 · see citing works
Alsobhi, M., Khan, F., Chevidikunnan, M. F., Basuodan, R., Shawli, L., & Neamatallah, Z. (2022) · Journal of Medical Internet Research · doi:10.2196/39565 · Cited by 64 · see citing works
Al Hadithy, Z. A., Al Lawati, A., Al-Zadjali, R., & Al Sinawi, H. (2023) · Cureus · doi:10.7759/cureus.44887 · Cited by 55 · see citing works
Zhai, H., Yang, X., Xue, J., Lavender, C., Ye, T., Li, J.-B., Xu, L., Lin, L., Cao, W., & Sun, Y. (2021) · Journal of Medical Internet Research · doi:10.2196/27122 · Cited by 57 · see citing works
Physicians’ required competencies in AI-assisted clinical settings: a systematic review
Schuitmaker, L., Drogt, J., Benders, M., & Jongsma, K. (2025) · British Medical Bulletin · doi:10.1093/bmb/ldae025 · Cited by 54 · see citing works
Moldt, J.-A., Festl-Wietek, T., Fuhl, W., Zabel, S., Claassen, M., Wagner, S., Nieselt, K., & Herrmann-Werner, A. (2024) · JMIR Medical Education · doi:10.2196/58355 · Cited by 47 · see citing works
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Watson, D., Womack, J., & Papadakos, S. (2020) · Critical Care Nursing Quarterly · doi:10.1097/cnq.0000000000000315 · Cited by 48 · see citing works
Mirkin, S., & Albensi, B. C. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1094233 · Cited by 46 · see citing works
Sarraf, B., & Ghasempour, A. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1628831 · Cited by 40 · see citing works
Galsgaard, A., Doorschodt, T., Holten, A.-L., Müller, F. C., Ploug Boesen, M., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110231 · Cited by 38 · see citing works
Ahmad, H. A., East, J. E., Panaccione, R., Travis, S., Canavan, J. B., Usiskin, K., & Byrne, M. F. (2023) · Intestinal Research · doi:10.5217/ir.2023.00020 · Cited by 37 · see citing works
Hesso, I., Kayyali, R., Dolton, D.-R., Joo, K., Zacharias, L., Charalambous, A., Lavdaniti, M., Stalika, E., Ajami, T., Acampa, W., et al. (2023) · Radiation Oncology · doi:10.1186/s13014-023-02351-z · Cited by 33 · see citing works
Yoo, J., Hur, S., Hwang, W., & Cha, W. C. (2023) · Healthcare Informatics Research · doi:10.4258/hir.2023.29.1.64 · Cited by 34 · see citing works
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Barlow, J., Sragi, Z., Rivera‐Rivera, G., Al‐Awady, A., Daşdöğen, Ü., Courey, M. S., & Kirke, D. N. (2024) · Otolaryngology–Head and Neck Surgery · doi:10.1002/ohn.636 · Cited by 25 · see citing works
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Liu, Y., Zhou, Q., Peng, B., Jiang, J., Fang, L., Weng, W., Wang, W., Wang, S., & Zhu, X. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.853845 · Cited by 23 · see citing works
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Daher, O. A., Dabbousi, A. A., Chamroukh, R., Saab, A. Y., Al Ayoubi, A. R., & Salameh, P. (2024) · Cureus · doi:10.7759/cureus.51466 · Cited by 16 · see citing works
Patrzyk, S., Bielecki, W., & Woźniacka, A. (2022) · Advances in Dermatology and Allergology · doi:10.5114/ada.2022.117738 · Cited by 13 · see citing works
Artificial intelligence skepticism in career domains
Adepoju, O. D., Tijani, B., & Karera, S. (2024) · International Journal for Digital Society · doi:10.20533/ijds.2040.2570.2024.0236 · Cited by 6 · see citing works
BANSAL, M., & JINDAL, A. (2022) · The National Medical Journal of India · doi:10.25259/nmji_208_20 · Cited by 7 · see citing works
A quantitative analysis of global AI medical studies: gaps in randomized controlled trials
Wang, Q., Yang, Y., Qiu, L., Xu, Y., Sun, R., Xue, P., & Qiao, Y. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02698-z · Cited by 4 · see citing works
“The application of arti fi cial intelligence (AI) in medicine has advanced rapidly, and many studies suggest that AI models can matchor even exceed clinician performance on speci fi c tasks 1 – 7 .”
Zhou, Z., Cheng, Y., Ren, Q., Li, Y., Yuanyue, X., Kang, J., Lu, C., & Ge, P. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1690534 · Cited by 1 · see citing works
“Artificial intelligence (AI) is revolutionizing the field of medicine by providing powerful tools for patient data analysis (26).”
- Authors
- Ahuja, A. S.
- Affiliations
- Florida Atlantic University (US)
- Corresponding author
- Ahuja, Abhimanyu S.
- ORCID
- Ahuja, Abhimanyu S.
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2019-10-04
- Volume
- 7
- Pages
- e7702
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC6779111
- OpenAlex
- W2977373448
- Cited by
- 789
- References
- 28
- Citations by year
- 2026: 59 · 2025: 136 · 2024: 197 · 2023: 152 · 2022: 111 · 2021: 99 · 2020: 35
- FWCI
- 19.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ (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 › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, physician role, physician-patient relationship, radiology, pathology, ophthalmology, future of medicine
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