메뉴 건너뛰기
JAMA - Journal of the American Medical Association
Volumn 322, Issue 24, 2019, Pages 2377-2378
Addressing Bias in Artificial Intelligence in Health Care
Author keywords

[No Author keywords available]
Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATION; CLINICAL DECISION MAKING; DATA SYNTHESIS; DECISION SUPPORT SYSTEM; HEALTH CARE DELIVERY; HEALTH CARE NEED; HEALTH CARE QUALITY; HUMAN; NOTE; PATIENT CARE; PRIORITY JOURNAL; REINFORCEMENT LEARNING (MACHINE LEARNING); SOCIAL BIAS; SOCIAL PSYCHOLOGY; STATISTICAL BIAS; SYSTEMATIC ERROR; TOTAL QUALITY MANAGEMENT; CLINICAL DECISION SUPPORT SYSTEM;
EID: 85075685806     PISSN: 00987484     EISSN: 15383598     Source Type: Journal    
DOI: 10.1001/jama.2019.18058     Document Type: Note
Times cited : (402)
References (8)
  • 1
    • 85053019174 scopus 로고    scopus 로고
    • Potential biases in machine learning algorithms using electronic health record data
    • doi: 30128552
    • Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018; 178 (11): 1544-1547. doi: 10.1001/jamainternmed.2018.3763 30128552
    • (2018) JAMA Intern Med , vol.178 , Issue.11 , pp. 1544-1547
    • Gianfrancesco, M.A.1    Tamang, S.2    Yazdany, J.3    Schmajuk, G.4
  • 2
    • 84940062157 scopus 로고    scopus 로고
    • Race/ethnic differences in the associations of the Framingham risk factors with carotid IMT and cardiovascular events
    • e0132321. doi: 26134404
    • Gijsberts CM, Groenewegen KA, Hoefer IE, Race/ethnic differences in the associations of the Framingham risk factors with carotid IMT and cardiovascular events. PLoS One. 2015; 10 (7):
    • (2015) PLoS One , vol.10 , Issue.7
    • Gijsberts, C.M.1    Groenewegen, K.A.2    Hoefer, I.E.3
  • 3
    • 37149043465 scopus 로고    scopus 로고
    • Symptom presentation of women with acute coronary syndromes: Myth vs reality
    • doi: 18071161
    • Canto JG, Goldberg RJ, Hand MM, Symptom presentation of women with acute coronary syndromes: myth vs reality. Arch Intern Med. 2007; 167 (22): 2405-2413. doi: 10.1001/archinte.167.22.2405 18071161
    • (2007) Arch Intern Med , vol.167 , Issue.22 , pp. 2405-2413
    • Canto, J.G.1    Goldberg, R.J.2    Hand, M.M.3
  • 4
    • 85046254953 scopus 로고    scopus 로고
    • Biases in electronic health record data due to processes within the healthcare system: Retrospective observational study
    • doi: 29712648
    • Agniel D, Kohane IS, Weber GM. Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ. 2018; 361: k1479. doi: 10.1136/bmj.k1479 29712648
    • (2018) BMJ , vol.361 , pp. k1479
    • Agniel, D.1    Kohane, I.S.2    Weber, G.M.3
  • 5
    • 84979500451 scopus 로고    scopus 로고
    • Health care segregation, physician recommendation, and racial disparities in BRCA1/2 testing among women with breast cancer
    • doi: 27161971
    • McCarthy AM, Bristol M, Domchek SM, Health care segregation, physician recommendation, and racial disparities in BRCA1/2 testing among women with breast cancer. J Clin Oncol. 2016; 34 (22): 2610-2618. doi: 10.1200/JCO.2015.66.0019 27161971
    • (2016) J Clin Oncol , vol.34 , Issue.22 , pp. 2610-2618
    • McCarthy, A.M.1    Bristol, M.2    Domchek, S.M.3
  • 6
    • 77958470996 scopus 로고    scopus 로고
    • Complacency and bias in human use of automation: An attentional integration
    • doi: 21077562
    • Parasuraman R, Manzey DH. Complacency and bias in human use of automation: an attentional integration. Hum Factors. 2010; 52 (3): 381-410. doi: 10.1177/0018720810376055 21077562
    • (2010) Hum Factors , vol.52 , Issue.3 , pp. 381-410
    • Parasuraman, R.1    Manzey, D.H.2
  • 7
    • 85061962448 scopus 로고    scopus 로고
    • Can AI help reduce disparities in general medical and mental health care?
    • doi: 30794127
    • Chen IY, Szolovits P, Ghassemi M. Can AI help reduce disparities in general medical and mental health care? AMA J Ethics. 2019; 21 (2): E167-E179. doi: 10.1001/amajethics.2019.167 30794127
    • (2019) AMA J Ethics , vol.21 , Issue.2 , pp. E167-E179
    • Chen, I.Y.1    Szolovits, P.2    Ghassemi, M.3
  • 8
    • 85059267182 scopus 로고    scopus 로고
    • PROBAST: A tool to assess the risk of bias and applicability of prediction model studies
    • doi: 30596875
    • Wolff RF, Moons KGM, Riley RD,; PROBAST Group. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019; 170 (1): 51-58. doi: 10.7326/M18-1376 30596875
    • (2019) Ann Intern Med , vol.170 , Issue.1 , pp. 51-58
    • Wolff, R.F.1    Moons, K.G.M.2    Riley, R.D.3

* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.