Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care
Abstract
The practice of medicine is changing rapidly as a consequence of electronic health record adoption, new technologies for patient care, disruptive innovations that breakdown professional hierarchies, and evolving societal norms. Collectively, these have resulted in the modification of the physician’s role as the gatekeeper for health care, increased shift-based care, and amplified interprofessional team-based care. Technological innovations present opportunities as well as challenges. Artificial intelligence, which has great potential, has already transformed some tasks, particularly those involving image interpretation. Ubiquitous access to information via the Internet by physicians and patients alike presents benefits as well as drawbacks: patients and providers have ready access to virtually all of human knowledge, but some websites are contaminated with misinformation and many people have difficulty differentiating between solid, evidence-based data and untruths. The role of the future physician will shift as complexity in health care increases and as artificial intelligence and other technologies advance. These technological advances demand new skills of physicians; memory and knowledge accumulation will diminish in importance while information management skills will become more important. In parallel, medical educators must enhance their teaching and assessment of critical human skills (e.g., clear communication, empathy) in the delivery of patient care. The authors emphasize the enduring role of critical human skills in safe and effective patient care even as medical practice is increasingly guided by artificial intelligence and related technology, and they suggest new and longitudinal ways of assessing essential noncognitive skills to meet the demands of the future. The authors envision practical and achievable benefits accruing to patients and providers if practitioners leverage technological advancements to facilitate the development of their critical human skills.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The costs of misusing AI may be equally problematic as its benefits.
“But the costs of misusing AI may be equally problematic. 60”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [60]
Cites
1 retracted
Panagioti, M., Geraghty, K., Johnson, J., Zhou, A. Y., Panagopoulou, E. P., Chew-Graham, C. A., Peters, D. H., Hodkinson, A., Riley, R. A., & Esmail, A. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3713 · Cited by 1,018 · see citing works
RetractedDermatologist-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
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
Big data analytics in healthcare: promise and potential
Raghupathi, W., & Raghupathi, V. (2014) · Health Information Science and Systems · doi:10.1186/2047-2501-2-3 · Cited by 3,034 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works
Emotional intelligence: New ability or eclectic traits?
Mayer, J. D., Salovey, P., & Caruso, D. R. (2008) · American Psychologist · doi:10.1037/0003-066x.63.6.503 · Cited by 1,792 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works
Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties
Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016) · Annals of Internal Medicine · doi:10.7326/m16-0961 · Cited by 1,494 · see citing works
The Evolution of Crew Resource Management Training in Commercial Aviation
Helmreich, R. L., Merritt, A. C., & Wilhelm, J. A. (1999) · International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0901_2 · Cited by 1,114 · see citing works
Shanafelt, T. D., Dyrbye, L. N., Sinsky, C., Hasan, O., Satele, D., Sloan, J., & West, C. P. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.05.007 · Cited by 1,086 · see citing works
Outcomes of Treatment for Hepatitis C Virus Infection by Primary Care Providers
Arora, S., Thornton, K., Murata, G. H., Deming, P., Kalishman, S. G., Dion, D., Parish, B. S., Burke, T., Pak, W., Dunkelberg, J. C., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1009370 · Cited by 1,077 · see citing works
Effectiveness of empathy in general practice: a systematic review
Derksen, F., Bensing, J., & Lagro-Janssen, A. (2013) · British Journal of General Practice · doi:10.3399/bjgp13x660814 · Cited by 1,034 · see citing works
(2019) · Cited by 986 · see citing works
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972 · see citing works
Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W.-J., Sinsky, C. A., & Gilchrist, V. J. (2017) · The Annals of Family Medicine · doi:10.1370/afm.2121 · Cited by 972 · see citing works
Yasaka, K., Akai, H., Abe, O., & Kiryu, S. (2018) · Radiology · doi:10.1148/radiol.2017170706 · Cited by 663 · see citing works
Brown, J. M., Campbell, J. P., Beers, A., Chang, K., Ostmo, S., Chan, R. V. P., Dy, J., Erdogmus, D., Ioannidis, S., Kalpathy-Cramer, J., et al. (2018) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2018.1934 · Cited by 657 · see citing works
Medical Education Must Move From the Information Age to the Age of Artificial Intelligence
Wartman, S. A., & Combs, C. D. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002044 · Cited by 585 · see citing works
Predicting Risk of Suicide Attempts Over Time Through Machine Learning
Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017) · Clinical Psychological Science · doi:10.1177/2167702617691560 · Cited by 571 · see citing works
Mori, Y., Kudo, S.-E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Ohtsuka, K., Urushibara, F., Kataoka, S., Ogawa, Y., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0249 · Cited by 509 · see citing works
Evaluation of symptom checkers for self diagnosis and triage: audit study
Semigran, H. L., Linder, J. A., Gidengil, C., & Mehrotra, A. (2015) · BMJ · doi:10.1136/bmj.h3480 · Cited by 489 · see citing works
Fully Automated Deep Learning System for Bone Age Assessment
Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9955-8 · Cited by 452 · see citing works
Googling for a diagnosis—use of Google as a diagnostic aid: internet based study
Tang, H., & Ng, J. (2006) · BMJ · doi:10.1136/bmj.39003.640567.ae · Cited by 386 · see citing works
AI Superpowers: China, Silicon Valley, and the New World Order
Lee, K.-F. (2018) · Virtual Defense Library (Ministerio de Defensa) · Cited by 359 · see citing works
Long, E., Lin, H., Liu, Z., Wu, X., Wang, L., Jiang, J., An, Y., Lin, Z., Li, X., Chen, J., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-016-0024 · Cited by 358 · see citing works
The State of Ultrasound Education in U.S. Medical Schools
Bahner, D. P., Goldman, E., Way, D. P., Royall, N. A., & Liu, Y. T. (2014) · Academic Medicine · doi:10.1097/acm.0000000000000414 · Cited by 356 · see citing works
The Potency of Team-Based Care Interventions for Hypertension
Carter, B. L., Rogers, M., Daly, J. M., Zheng, S., & James, P. A. (2009) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.316 · Cited by 354 · see citing works
Mapping the anti-vaccination movement on Facebook
Smith, N., & Graham, T. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2017.1418406 · Cited by 351 · see citing works
Residents’ and Attending Physicians’ Handoffs: A Systematic Review of the Literature
Riesenberg, L. A., Leitzsch, J., Massucci, J. L., Jaeger, J., Rosenfeld, J. C., Patow, C., Padmore, J. S., & Karpovich, K. P. (2009) · Academic Medicine · doi:10.1097/acm.0b013e3181bf51a6 · Cited by 345 · see citing works
Individualized Medicine from Prewomb to Tomb
Topol, E. J. (2014) · Cell · doi:10.1016/j.cell.2014.02.012 · Cited by 340 · see citing works
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
Lee, S., Çelik, S., Logsdon, B. A., Lundberg, S., Martins, T. J., Oehler, V. G., Estey, E. H., Miller, C. P., Chien, S. S., Dai, J., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-02465-5 · Cited by 332 · see citing works
A systematic review of tests of empathy in medicine
Hemmerdinger, J. M., STODDART, S. D. R., & Lilford, R. J. (2007) · BMC Medical Education · doi:10.1186/1472-6920-7-24 · Cited by 323 · see citing works
Automated Detection of Adverse Events Using Natural Language Processing of Discharge Summaries
Melton, G. B., & Hripcsak, G. M. (2005) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1794 · Cited by 298 · see citing works
Assessment drives learning: An unavoidable truth?
Wormald, B. W., Schoeman, S., Somasunderam, A., & Penn, M. (2009) · Anatomical Sciences Education · doi:10.1002/ase.102 · Cited by 292 · see citing works
Are Physicians Ready for Patients With Internet-Based Health Information?
Ahmad, F., Hudak, P. L., Bercovitz, K., Hollenberg, E., & Levinson, W. (2006) · Journal of Medical Internet Research · doi:10.2196/jmir.8.3.e22 · Cited by 283 · see citing works
The Patient Will See You Now: The Future of Medicine is in Your Hands
Parish, J. (2015) · Journal of Clinical Sleep Medicine (JCSM) · doi:10.5664/jcsm.4788 · Cited by 273 · see citing works
Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-01931-w · Cited by 154 · see citing works
Two Decades of Organizational Change in Health Care: What Have we Learned?
Bazzoli, G. J., Dynan, L., Burns, L. R., & Yap, C. (2004) · Medical Care Research and Review · doi:10.1177/1077558704266818 · Cited by 137 · see citing works
Rapport, F., Hibbert, P. D., Baysari, M. T., et al. (2019) · BMC Health Services Research · doi:10.1186/s12913-019-3881-z · Cited by 93 · see citing works
Johnston, S. C. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002175 · Cited by 90 · see citing works
Cullen, R. J., Clark, M., & Esson, R. (2011) · Health Information & Libraries Journal · doi:10.1111/j.1471-1842.2011.00933.x · Cited by 88 · see citing works
Humanism in Medicine: What Does It Mean and Why Is It More Important Than Ever?
Thibault, G. E. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002796 · Cited by 83 · see citing works
Safe Infant Sleep Recommendations on the Internet: Let's Google It
Chung, M., Oden, R. P., Joyner, B. L., Corley, A. M. S., & Moon, R. Y. (2012) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2012.06.004 · Cited by 81 · see citing works
Watson Will See You Now: A Supercomputer to Help Clinicians Make Informed Treatment Decisions
Doyle-Lindrud, S. (2015) · Clinical journal of oncology nursing · doi:10.1188/15.cjon.31-32 · Cited by 63 · see citing works
Gupte, G., Vimalananda, V., Simon, S. R., DeVito, K., Clark, J., & Orlander, J. D. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.4801 · Cited by 63 · see citing works
The Emergence Of Retail-Based Clinics In The United States: Early Observations
Laws, M., & Scott, M. (2008) · Health Affairs · doi:10.1377/hlthaff.27.5.1293 · Cited by 51 · see citing works
Automated analysis of retinal imaging using machine learning techniques for computer vision
Fauw, J. D., Keane, P. A., Tomašev, N., Visentin, D., van den Driessche, G., Johnson, M., Hughes, C., Chu, C., Ledsam, J. R. E., Back, T., et al. (2016) · F1000Research · doi:10.12688/f1000research.8996.1 · Cited by 48 · see citing works
Automated problem list generation and physicians perspective from a pilot study
Devarakonda, M. V., Mehta, N., Tsou, C.-H., Liang, J. J., Nowacki, A. S., & Jelovsek, J. E. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.05.015 · Cited by 45 · see citing works
An interpretable natural language processing system for written medical examination assessment
Sarker, A., Klein, A. Z., Mee, J., Harik, P., & Gonzalez‐Hernandez, G. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103268 · Cited by 44 · see citing works
Maggio, L. A., & Kung, J. Y. C. (2014) · Journal of the Medical Library Association JMLA · doi:10.3163/1536-5050.102.3.008 · Cited by 39 · see citing works
A Methodological Review of the Assessment of Humanism in Medical Students
Buck, E., Holden, M. D., & Szauter, K. M. (2015) · Academic Medicine · doi:10.1097/acm.0000000000000910 · Cited by 37 · see citing works
The computer will see you now.
Pelosi, A. J., & Lewis, G. (1989) · BMJ · doi:10.1136/bmj.299.6692.138 · Cited by 28 · see citing works
Hicks, P. J., Margolis, M. J., Poynter, S. E., Chaffinch, C. N., Tenney‐Soeiro, R., Turner, T. L., Waggoner-Fountain, L. A., Lockridge, R., Clyman, S. G., & Schwartz, A. (2016) · Academic Medicine · doi:10.1097/acm.0000000000001057 · Cited by 28 · see citing works
Turner, T. L., Bhavaraju, V., Luciw-Dubas, U. A., et al. (2017) · Academic Medicine · doi:10.1097/acm.0000000000001622 · Cited by 27 · see citing works
Haruna, H., & Hu, X. (2018) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2017.12.004 · Cited by 19 · see citing works
Learning through Osmosis: A collaborative platform for medical education
Haynes, M. R., Gaglani, S. M., Wilcox, M. V., Mitchell, T., DeLeon, V. B., & Goldberg, H. R. (2014) · Innovations in Global Medical and Health Education · doi:10.5339/igmhe.2014.2 · Cited by 18 · see citing works
Emotional intelligence scale for medical students
Srivastava, K., Joshi, S. V., Raichaudhuri, A., Ryali, V., Bhat, P., Shashikumar, R., Prakash, J., & Basannar, D. R. (2011) · Industrial Psychiatry Journal · doi:10.4103/0972-6748.98413 · Cited by 17 · see citing works
Quality measures and pediatric radiology: suggestions for the transition to value-based payment
Heller, R. E., Coley, B. D., Simoneaux, S. F., et al. (2017) · Pediatric Radiology · doi:10.1007/s00247-017-3857-2 · Cited by 16 · see citing works
Looking Ahead: Futures Planning for Medical Education
Sklar, D. P. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002861 · Cited by 15 · see citing works
New Physicians Will Need Business School Skills
Pearl, R. M., & Fogel, A. L. (2017) · NEJM Catalyst · Cited by 14 · see citing works
Medicine in 2035: Selected Insights From ACGME's Scenario Planning
Nasca, T. J., & Thomas, C. W. (2014) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-14-00740.1 · Cited by 12 · see citing works
The CVS–Aetna Merger: Another Large Bet on the Changing U.S. Health Care Landscape
Frakt, A. B., & Garthwaite, C. (2018) · Annals of Internal Medicine · doi:10.7326/m17-3319 · Cited by 8 · see citing works
Kash, M. J. (2016) · Medical Reference Services Quarterly · doi:10.1080/02763869.2016.1152148 · Cited by 6 · see citing works
Heart Signal Abnormality Detection Using Artificial Neural Networks1
Haider, A., & Fazel-Rezai, R. (2014) · Journal of Medical Devices · doi:10.1115/1.4027015 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Challenges and opportunities for educating health professionals after the COVID-19 pandemic
Frenk, J., Chen, L. C., Chandran, L., Groff, E. O. H., King, R., Meleis, A., & Fineberg, H. V. (2022) · The Lancet · doi:10.1016/s0140-6736(22)02092-x · Cited by 369 · see citing works
Self-citationCompetencies for the Use of Artificial Intelligence–Based Tools by Health Care Professionals
Russell, R. G., Lovett Novak, L., Patel, M., Garvey, K. V., Craig, K. J. T., Jackson, G. P., Moore, D., & Miller, B. M. (2023) · Academic Medicine · doi:10.1097/acm.0000000000004963 · Cited by 320 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
“But the costs of misusing AI may be equally problematic.(60) These advantages and liabilities call for future physicians to begin to be properly trained in AI’s uses and pitfalls.”
BackgroundPhysicians’ 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
Henzler, D., Schmidt, S., Koçar, A., Herdegen, S., Lindinger, G. L., Maris, M. T., Bak, M. A. R., Willems, D. L., Tan, H. L., Lauerer, M., et al. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12664-2 · Cited by 54 · see citing works
“However, to compensate the use of AI will demand the development of new skills of healthcare professionals, e.g. information management skills, and strengthen the skills that AI most likely cannot substitute for such as communication capabilities and empathy [98].”
Jacobs, S. M., Lundy, N. N., Issenberg, S. B., & Chandran, L. (2023) · JMIR Medical Education · doi:10.2196/50903 · Cited by 33 · see citing works
Self-citation
- Authors
- Alrassi, J., Katsufrakis, P. J., & Chandran, L.
- Affiliations
- SUNY Downstate Health Sciences University (US)
- National Board of Medical Examiners (US)
- University of Miami (US)
- Corresponding author
- Chandran, Latha
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12-29
- Volume
- 96
- Issue
- 1
- Pages
- 37-43
- ISSN
- 1040-2446 (ISSN-L)
- OpenAlex
- W3084074804
- Cited by
- 60
- References
- 66
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 13 · 2023: 7 · 2022: 9 · 2021: 5
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Psychiatry and Mental health › Empathy and Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- non-cognitive skills, artificial intelligence in healthcare, physician role, electronic health records, interprofessional team care, medical education, empathy, communication skills, health misinformation, physician-patient communication
- MeSH terms
- Adult, Artificial Intelligence, Clinical Competence, Empathy, Female, Patient Care, Humans, Male, Middle Aged, Physician's Role, Physicians, Therapy, Computer-Assisted
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review