An awakening in medicine: the partnership of humanity and intelligent machines
Open accessAbstract
In concurrence with the introduction of the internet, widely networked computers, and the collection of large amounts of digital data, the medical profession as a whole has become more self-aware and self-critical. It is increasingly apparent that suboptimal decisions are made at times and, on other occasions, are fatally flawed. Most clinical decisions rest largely on what is referred to as the art of medicine: that is, decision-making that is based on inconsistent and incomplete provider knowledge; variable skills, training, and experience; and last but not the least, an array of biases.
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The simulated environment is unable to capture the full complexity of high-risk decision-making in a live-care delivery setting like the hospital environment, where there are many competing time and attention demands.
“However, the simulated environment is unable to capture the full complexity of high-risk decision-making in a live-care delivery setting like the hospital environment, where there are many competing time and attention demands 4,15,16,29 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [15]
The paper’s main text (page not provided) describes complex clinical decisions and warns that poorly designed AI could worsen information overload and cognitive fatigue in clinical workflows. It does not discuss simulated environments or establish that they fail to capture the competing time and attention demands of live hospital care. (LLM, conf=0.76)
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Simon, G. E., Johnson, E., Lawrence, J. M., Rossom, R. C., Ahmedani, B., Lynch, F. L., Beck, A., Waitzfelder, B., Ziebell, R., Penfold, R. B., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17101167 · Cited by 400 · see citing works
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Research works printed but not linked: 9
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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
Bachtiger, P., Petri, C. F., Scott, F. E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P. S., Hatrick, I. R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00256-9 · Cited by 167 · see citing works
Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01895-z · Cited by 127 · see citing works
Salwei, M. E., & Carayon, P. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221097357 · Cited by 112 · see citing works
McLennan, S., Meyer, A., Schreyer, K., & Buyx, A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000114 · Cited by 50 · see citing works
- Authors
- Celi, L. A., Fine, B., & Stone, D. J.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 1
- Issue
- 6
- Pages
- e255-e257
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7331949
- OpenAlex
- W2975366483
- Cited by
- 45
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 6 · 2022: 7 · 2021: 12 · 2020: 8 · 2019: 3 · 2018: 1
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5r01eb017205-07)
- National Institute of Biomedical Imaging and Bioengineering (R01-EB017205)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, clinical decision-making, human-AI collaboration, cognitive biases, digital health data
- MeSH terms
- Artificial Intelligence, Humans, Humanities, Medicine
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