Clinician Cognition and Artificial Intelligence in Medicine

William W. Feaster; Kevin Maher; John Lee; Addison Gearhart; Alan S. Young; Allana Cummings; Bill Vorhies; Chris Yoo; Roderic Ivan Pettigrew; Piyush Mathur; Francis Papay; K. C. Cummings; Hamilton Baker; Danton Char; Kevin Seals; Arlen Meyer; Peter R. Holbrook; Geoffrey W. Rutledge; Todd Feinman; Louis Agha-Mir-Salim; Leo Anthony Celi; Nina Miolane; David M. Axelrod; Sybil Klaus; Alison Callahan; Nigam H. Shah; Sharib Gaffar; Leila Entezam

doi:10.1016/B978-0-12-823337-5.00007-X

Clinician Cognition and Artificial Intelligence in Medicine

William W. Feaster, Kevin Maher, John Lee, Addison Gearhart, Alan S. Young, Allana Cummings, Bill Vorhies, Chris Yoo, Roderic Ivan Pettigrew, Piyush Mathur, Francis Papay, K. C. Cummings, Hamilton Baker, Danton Char, Kevin Seals, Arlen Meyer, Peter R. Holbrook, Geoffrey W. Rutledge, Todd Feinman, Louis Agha-Mir-SalimLeo Anthony Celi, Nina Miolane, David M. Axelrod, Sybil Klaus, Alison Callahan, Nigam H. Shah, Sharib Gaffar, Leila Entezam

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

There is a myriad of reasons for there to be a more intelligent paradigm in medicine with the adoption of artificial intelligence: augmenting clinician knowledge and expertise; decreasing clinical and administrative burden; facilitating care coordination; and mitigating clinician burnout. There is a concomitantly long list of challenges for artificial intelligence adoption that pertain to data and databases, technology, stakeholders, and other issues such as bias and ethics. Clinician cognition will be more important than ever before with the advent of artificial intelligence. The clinician’s brain has several elements: perception, cognition, and operation, and these are used in various proportions depending on the subspecialty. Aspects of clinical medicine such as complexity, uncertainty as well as biases and heuristics will be additional challenges for medicine in the future. Evidence-based medicine and its limitations are discussed in the context of a new paradigm of intelligence-based medicine. Current applications of artificial intelligence in medicine and health care such as medical imaging, decision support, precision medicine, and altered reality, and robotic technology are briefly discussed.

Original language	English (US)
Title of host publication	Intelligence-Based Medicine
Subtitle of host publication	Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare
Publisher	Elsevier
Pages	193-266
Number of pages	74
ISBN (Electronic)	9780128233375
DOIs	https://doi.org/10.1016/B978-0-12-823337-5.00007-X
State	Published - Jan 1 2020

Keywords

Cognition
Evidence-based medicine
Intelligence-based medicine
Perception

ASJC Scopus subject areas

General Medicine
General Health Professions

Access to Document

10.1016/B978-0-12-823337-5.00007-X

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Feaster, W. W., Maher, K., Lee, J., Gearhart, A., Young, A. S., Cummings, A., Vorhies, B., Yoo, C., Pettigrew, R. I., Mathur, P., Papay, F., Cummings, K. C., Baker, H., Char, D., Seals, K., Meyer, A., Holbrook, P. R., Rutledge, G. W., Feinman, T., ... Entezam, L. (2020). Clinician Cognition and Artificial Intelligence in Medicine. In Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare (pp. 193-266). Elsevier. https://doi.org/10.1016/B978-0-12-823337-5.00007-X

Feaster, WW, Maher, K, Lee, J, Gearhart, A, Young, AS, Cummings, A, Vorhies, B, Yoo, C, Pettigrew, RI, Mathur, P, Papay, F, Cummings, KC, Baker, H, Char, D, Seals, K, Meyer, A, Holbrook, PR, Rutledge, GW, Feinman, T, Agha-Mir-Salim, L, Celi, LA, Miolane, N, Axelrod, DM, Klaus, S, Callahan, A, Shah, NH, Gaffar, S & Entezam, L 2020, Clinician Cognition and Artificial Intelligence in Medicine. in Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare. Elsevier, pp. 193-266. https://doi.org/10.1016/B978-0-12-823337-5.00007-X

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abstract = "There is a myriad of reasons for there to be a more intelligent paradigm in medicine with the adoption of artificial intelligence: augmenting clinician knowledge and expertise; decreasing clinical and administrative burden; facilitating care coordination; and mitigating clinician burnout. There is a concomitantly long list of challenges for artificial intelligence adoption that pertain to data and databases, technology, stakeholders, and other issues such as bias and ethics. Clinician cognition will be more important than ever before with the advent of artificial intelligence. The clinician{\textquoteright}s brain has several elements: perception, cognition, and operation, and these are used in various proportions depending on the subspecialty. Aspects of clinical medicine such as complexity, uncertainty as well as biases and heuristics will be additional challenges for medicine in the future. Evidence-based medicine and its limitations are discussed in the context of a new paradigm of intelligence-based medicine. Current applications of artificial intelligence in medicine and health care such as medical imaging, decision support, precision medicine, and altered reality, and robotic technology are briefly discussed.",

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doi = "10.1016/B978-0-12-823337-5.00007-X",

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AU - Feaster, William W.

AU - Maher, Kevin

AU - Lee, John

AU - Gearhart, Addison

AU - Young, Alan S.

AU - Cummings, Allana

AU - Vorhies, Bill

AU - Yoo, Chris

AU - Pettigrew, Roderic Ivan

AU - Mathur, Piyush

AU - Papay, Francis

AU - Cummings, K. C.

AU - Baker, Hamilton

AU - Char, Danton

AU - Seals, Kevin

AU - Meyer, Arlen

AU - Holbrook, Peter R.

AU - Rutledge, Geoffrey W.

AU - Feinman, Todd

AU - Agha-Mir-Salim, Louis

AU - Celi, Leo Anthony

AU - Miolane, Nina

AU - Axelrod, David M.

AU - Klaus, Sybil

AU - Callahan, Alison

AU - Shah, Nigam H.

AU - Gaffar, Sharib

AU - Entezam, Leila

PY - 2020/1/1

Y1 - 2020/1/1

N2 - There is a myriad of reasons for there to be a more intelligent paradigm in medicine with the adoption of artificial intelligence: augmenting clinician knowledge and expertise; decreasing clinical and administrative burden; facilitating care coordination; and mitigating clinician burnout. There is a concomitantly long list of challenges for artificial intelligence adoption that pertain to data and databases, technology, stakeholders, and other issues such as bias and ethics. Clinician cognition will be more important than ever before with the advent of artificial intelligence. The clinician’s brain has several elements: perception, cognition, and operation, and these are used in various proportions depending on the subspecialty. Aspects of clinical medicine such as complexity, uncertainty as well as biases and heuristics will be additional challenges for medicine in the future. Evidence-based medicine and its limitations are discussed in the context of a new paradigm of intelligence-based medicine. Current applications of artificial intelligence in medicine and health care such as medical imaging, decision support, precision medicine, and altered reality, and robotic technology are briefly discussed.

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Clinician Cognition and Artificial Intelligence in Medicine

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Keywords

ASJC Scopus subject areas

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