TY - JOUR
T1 - Evidence-based medicine and machine learning: a partnership with a common purpose
AU - Scott, Ian
AU - Cook, David
AU - Coiera, Enrico
PY - 2020/8/19
Y1 - 2020/8/19
N2 - From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of hypothesis-driven empirical research used to evaluate the utility of diagnostic tests, prognostic tools and therapeutic interventions. Machine learning, a subset of artificial intelligence, uses computer programs to discover patterns and associations within huge datasets which are then incorporated into algorithms used to assist diagnoses and predict future outcomes, including response to therapies. How do these two fields relate to one another? What are their similarities and differences, their strengths and weaknesses? Can each learn from, and complement, the other in rendering clinical decision-making more informed and effective?
AB - From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of hypothesis-driven empirical research used to evaluate the utility of diagnostic tests, prognostic tools and therapeutic interventions. Machine learning, a subset of artificial intelligence, uses computer programs to discover patterns and associations within huge datasets which are then incorporated into algorithms used to assist diagnoses and predict future outcomes, including response to therapies. How do these two fields relate to one another? What are their similarities and differences, their strengths and weaknesses? Can each learn from, and complement, the other in rendering clinical decision-making more informed and effective?
U2 - 10.1136/bmjebm-2020-111379
DO - 10.1136/bmjebm-2020-111379
M3 - Article
C2 - 32816901
JO - BMJ Evidence-Based Medicine
JF - BMJ Evidence-Based Medicine
SN - 2515-446X
ER -