Artificial intelligence in medical practice: the question to the answer?
Abstract
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.
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AI algorithms perform on par with or better than humans in various tasks, such as analyzing medical images or correlating symptoms and biomarkers from electronic medical records with disease characterization and prognosis.
“There is already a large amount of evidence that AI algorithms are performing on par or better than humans in various tasks, for instance, in analyzing medical images or correlating symptoms and biomarkers from electronic medical records (EMRs) with the characterization and prognosis of the disease [1].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [1]
AI algorithms can perform equally or better than humans in analyzing medical images or associating symptoms and biomarkers from electronic medical records with disease characterization and prognosis.
“Another paper declared that various tasks, such as analyzing medical images or associating symptoms and biomarkers from electronic medical records with the disease's characterization and prognosis, have shown that AI algorithms can perform equally or better than humans. 51”
From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [51]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., AlMazroa, M. A., Amann, M., Anderson, H. R., Andrews, K. G., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61766-8 · Cited by 11,805 · see citing works
(2016) · Cited by 11,679 · see citing works
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works
Roger, V. L. (2013) · Circulation Research · doi:10.1161/circresaha.113.300268 · Cited by 1,728 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
The Diagnostic Performance of Computer Programs for the Interpretation of Electrocardiograms
Willems, J. L., Abreu‐Lima, C., Arnaud, P., van Bemmel, J. H., Brohet, C., Degani, R., Denis, B., Gehring, J., Graham, I. M., van Herpen, G., et al. (1991) · New England Journal of Medicine · doi:10.1056/nejm199112193252503 · Cited by 538 · see citing works
Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction
Baxt, W. G. (1991) · Annals of Internal Medicine · doi:10.7326/0003-4819-115-11-843 · Cited by 474 · see citing works
Deep learning of the tissue-regulated splicing code
Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu277 · Cited by 459 · see citing works
Predicting healthcare trajectories from medical records: a deep learning approach
Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.001 · Cited by 447 · see citing works
Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Shen, D. G., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P., & Khan, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9 · Cited by 395 · see citing works
Tu, J. V., & Guerriere, M. (1993) · Computers and Biomedical Research · doi:10.1006/cbmr.1993.1015 · Cited by 137 · see citing works
Schnyer, D. M., Clasen, P. C., Gonzalez, C. E., & Beevers, C. G. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.03.003 · Cited by 70 · see citing works
Bishop, T. F., Ramsay, P. P., Casalino, L. P., Bao, Y., Pincus, H. A., & Shortell, S. M. (2016) · Health Affairs · doi:10.1377/hlthaff.2015.1068 · Cited by 49 · see citing works
Technology insertion in the defence industry: A primer
Kerr, C. I. V., Phaal, R., & Probert, D. R. (2008) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1243/09544054jem1080 · Cited by 32 · see citing works
Toward Generating Domain-Specific / Personalized Problem Lists from Electronic Medical Records.
Tsou, C.-H., Devarakonda, M. V., & Liang, J. J. (2015) · National Conference on Artificial Intelligence · Cited by 8 · see citing works
Deep Learning for Identifying Breast Cancer
Li, Y. (2020) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636 · see citing works
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works
Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?
Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., et al. (2022) · Frontiers in Surgery · doi:10.3389/fsurg.2022.862322 · Cited by 931 · see citing works
Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works
Artificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626 · see citing works
Artificial intelligence and computational pathology
Cui, M., & Zhang, D. Y. (2021) · Laboratory Investigation · doi:10.1038/s41374-020-00514-0 · Cited by 544 · see citing works
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479 · see citing works
The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries
Guo, J., & Li, B. (2018) · Health Equity · doi:10.1089/heq.2018.0037 · Cited by 401 · see citing works
Physician Confidence in Artificial Intelligence: An Online Mobile Survey
Oh, S., Kim, J. H., Choi, S.-W., Lee, H. J., Hong, J., & Kwon, S. H. (2019) · Journal of Medical Internet Research · doi:10.2196/12422 · Cited by 387 · see citing works
Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review
Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., He, Z., Wong, S. Y., Fang, P.-H., & Ming, W.-K. (2019) · JMIR Medical Informatics · doi:10.2196/10010 · Cited by 373 · see citing works
Chang, C., Hwang, G., & Gau, M. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13158 · Cited by 294 · see citing works
Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02204-y · Cited by 294 · see citing works
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Health Services Research and Managerial Epidemiology · doi:10.1177/23333928241234863 · Cited by 265 · see citing works
“51 Moreover, vision transformers, a deep learning model, have become popular in medical imaging, especially for lung cancer.”
BackgroundPublic Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020) · Journal of Medical Internet Research · doi:10.2196/16649 · Cited by 221 · see citing works
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., et al. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00233 · Cited by 196 · see citing works
Putting the data before the algorithm in big data addressing personalized healthcare
Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0157-2 · Cited by 149 · see citing works
Xie, Y., Chen, M., Kao, D., Gao, G., & Chen, X. '. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376807 · Cited by 141 · see citing works
Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125 · see citing works
Fonseka, T. M., Bhat, V., & Kennedy, S. H. (2019) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867419864428 · Cited by 126 · see citing works
Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_31_18 · Cited by 119 · see citing works
Higgins, D., & Madai, V. I. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000052 · Cited by 118 · see citing works
Next-generation business models for artificial intelligence start-ups in the healthcare industry
Kulkov, I. (2023) · International Journal of Entrepreneurial Behavior & Research · doi:10.1108/ijebr-04-2021-0304 · Cited by 97 · see citing works
Tarumi, S., Takeuchi, W., Chalkidis, G., Rodriguez-Loya, S., Kuwata, J., Flynn, M., Turner, K. M., Sakaguchi, F. H., Weir, C., Kramer, H., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1728757 · Cited by 84 · see citing works
Taira, K., Itaya, T., & Hanada, A. (2023) · JMIR Nursing · doi:10.2196/47305 · Cited by 76 · see citing works
Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning
Koçak, B., Cuocolo, R., Santos, D. P. D., Stanzione, A., & Ugga, L. (2023) · Balkan Medical Journal · doi:10.4274/balkanmedj.galenos.2022.2022-11-51 · Cited by 64 · see citing works
Machine intelligence in cardiovascular medicine
Miller, D. D. (2020) · Cardiology in Review · doi:10.1097/crd.0000000000000294 · Cited by 58 · see citing works
Self-citationZhao, Z., Wu, C.-M., Zhang, S., He, F., Liu, F., Wang, B., Huang, Y., Shi, W., Jian, D., Xie, H., et al. (2021) · JMIR Medical Informatics · doi:10.2196/23415 · Cited by 58 · see citing works
“In recent years, with the rapid development of computer science, artificial intelligence has emerged as a promising tool for face recognition, image analysis, and deciphering genomics [9-13].”
BackgroundBashkirova, A., & Krpan, D. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100066 · Cited by 55 · see citing works
Alwadani, F., Lone, A., Hakami, M., Moria, A., Alamer, W., Alghirash, R., Alnawah, A., & Hadadi, A. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s455260 · Cited by 30 · see citing works
Luo, Y., Weng, H., Yang, L., Ding, Z., & Wang, Q. (2023) · JMIR Formative Research · doi:10.2196/50413 · Cited by 28 · see citing works
Large language models in physical therapy: time to adapt and adept
Naqvi, W. M., Shaikh, S. Z., & Mishra, G. V. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1364660 · Cited by 24 · see citing works
- Authors
- Miller, D. D., & Brown, E. W.
- Affiliations
- New York Medical College (US)
- IBM (United States) (US)
- Corresponding author
- Miller, D. Douglas
- ORCID
- Miller, D. Douglas
- Venue
- The American Journal of Medicine
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 131
- Issue
- 2
- Pages
- 129-133
- ISSN
- 0002-9343 (ISSN-L)
- OpenAlex
- W2767381938
- Cited by
- 774
- References
- 22
- Citations by year
- 2026: 49 · 2025: 94 · 2024: 125 · 2023: 122 · 2022: 102 · 2021: 101 · 2020: 96 · 2019: 62 · 2018: 23
- FWCI
- 28.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The American Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Touro Scholar (Touro College) (submittedVersion)
- 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, medical image analysis, radiology, pathology, dermatology, natural language processing, electronic health records, clinical decision support, big data, machine learning, diagnostic accuracy, chronic disease management, precision medicine, clinical trial recruitment, medical errors
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Chronic Disease, Clinical Trials as Topic, Diagnosis, Computer-Assisted, Humans, Image Processing, Computer-Assisted, Natural Language Processing, Patient Selection, Medical Errors, Biostatistics, Precision Medicine, Electronic Health Records
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