Artificial intelligence in healthcare: An essential guide for health leaders
Abstract
Artificial Intelligence (AI) is evolving rapidly in healthcare, and various AI applications have been developed to solve some of the most pressing problems that health organizations currently face. It is crucial for health leaders to understand the state of AI technologies and the ways that such technologies can be used to improve the efficiency, safety, and access of health services, achieving value-based care. This article provides a guide to understand the fundamentals of AI technologies (ie, machine learning, natural language processing, and AI voice assistants) as well as their proper use in healthcare. It also provides practical recommendations to help decision-makers develop an AI strategy that can support their digital healthcare transformation.
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Pathology is evolving into digital pathology, which involves digitizing histopathology, immunohistochemistry, and cytology slides and training AI systems on this digital data.
“Pathology is evolving into digital pathology, which involves digitizing histopathology, immunohistochemistry, and cytology slides and training AI systems on this digital data (39).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [39]
Cites
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Conversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · cited 1× · see citing works
“However, the reliability of such text-based chatbots is often achieved at the cost of restricting user input to predetermined words and phrases, not allowing users to take the initiative in the dialogue.(6) With the high expectation of investors and the efforts from both big tech companies and start-ups, it is certain…”
BackgroundA Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
Yala, A., Lehman, C., Schuster, T., Portnoi, T., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2019182716 · Cited by 758 · see citing works
AI super-powers: China, Silicon Valley and the new world order
Klingler‐Vidra, R. (2019) · International Affairs · doi:10.1093/ia/iiz011 · Cited by 618 · see citing works
Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas
Korfiatis, P. D., & Erickson, B. J. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.01.028 · Cited by 57 · see citing works
Safety First: Conversational Agents for Health Care
Bickmore, T., Trinh, H., Asadi, R., & Olafsson, S. (2018) · Human–Computer Interaction Series · doi:10.1007/978-3-319-95579-7_3 · Cited by 50 · see citing works
Big Data, Big Challenges: A Healthcare Perspective
(2019) · Lecture notes in bioengineering · doi:10.1007/978-3-030-06109-8 · Cited by 18 · see citing works
Artificial Intelligence: Past, Present and Future
Soldatenko, D. M. (2020) · RePEc: Research Papers in Economics · Cited by 14 · see citing works
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Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08215-8 · Cited by 434 · see citing works
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
Artificial Intelligence Applications in Health Care Practice: Scoping Review
Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022) · Journal of Medical Internet Research · doi:10.2196/40238 · Cited by 198 · see citing works
Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future
Rony, M. K. K., Parvin, M. R., & Ferdousi, S. (2024) · Nursing Open · doi:10.1002/nop2.2070 · Cited by 202 · see citing works
Rony, M. K. K., Kayesh, I., Bala, S. D., Akter, F., & Parvin, M. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25718 · Cited by 190 · see citing works
Wubineh, B. Z., Deriba, F. G., & Woldeyohannis, M. M. (2024) · Urologic Oncology: Seminars and Original Investigations · doi:10.1016/j.urolonc.2023.11.019 · Cited by 141 · see citing works
Artificial Intelligence for Health Professions Educators
Lomis, K., Jeffries, P., Palatta, A., Sage, M., Sheikh, J., Sheperis, C., & Whelan, A. (2021) · NAM Perspectives · doi:10.31478/202109a · Cited by 136 · see citing works
Kansal, R., Bawa, A., Bansal, A., Trehan, S., Goyal, K., Goyal, N., & Malhotra, K. (2022) · Cureus · doi:10.7759/cureus.21434 · Cited by 76 · see citing works
Kim, M., Kim, S., Kim, J., Song, T.-J., & Kim, Y. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103160 · Cited by 53 · see citing works
Laukka, E., Hammarén, M., & Kanste, O. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13769 · Cited by 44 · see citing works
Wenderott, K., Krups, J., Luetkens, J. A., & Weigl, M. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104243 · Cited by 33 · see citing works
Eminoğlu, A., & Çelikkanat, Ş. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105386 · Cited by 30 · see citing works
Promoting health equity through improved regulation of artificial intelligence medical devices
Dortche, K., McCarthy, G., Banbury, S., & Yannatos, I. (2023) · Journal of Science Policy & Governance · doi:10.38126/jspg210302 · Cited by 14 · see citing works
Development of a practical training method for a healthcare artificial intelligence (AI) chatbot
Lin, J., Joseph, T., Parga-Belinkie, J. J., Mandel, A., Schumacher, R., Neumann, K., Scalise, L., Gaulton, J., Christ, L., Leitner, K., et al. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2020-000530 · Cited by 13 · see citing works
Rudra, A., & Agrawal, M. (2025) · 2025 International Conference on Computer and Applications (ICCA) · doi:10.1109/icca66035.2025.11430973 · Cited by 1 · see citing works
- Authors
- Chen, M., & Decary, M.
- Affiliations
- Cogilex R&D Inc., Montreal, Quebec, Canada.
- Corresponding author
- Chen, Mei
- ORCID
- Chen, Mei
- Venue
- Healthcare Management Forum
- Publisher
- SAGE Publications
- Published
- 2019-09-24
- Volume
- 33
- Issue
- 1
- Pages
- 10-18
- ISSN
- 0840-4704 (ISSN-L)
- 2352-3883
- OpenAlex
- W2977181930
- Semantic Scholar
- 48b44b31b4d378d73bbdcf3c0a38346fea578177
- Cited by
- 538
- References
- 9
- Citations by year
- 2026: 61 · 2025: 126 · 2024: 153 · 2023: 112 · 2022: 59 · 2021: 20 · 2020: 7
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Healthcare Management Forum (publishedVersion)
- 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, healthcare leadership, machine learning, natural language processing, voice assistants, AI strategy, value-based care, health care access
- MeSH terms
- Machine Learning, Artificial Intelligence, Delivery of Health Care, Humans, Natural Language Processing, Robotics, Speech Recognition Software
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