What makes a 'good' decision with artificial intelligence? A grounded theory study in paediatric care.

Journal: BMJ evidence-based medicine

Published Date: May 20, 2025

OBJECTIVE: To develop a framework for good clinical decision-making using machine learning (ML) models for interventional, patient-level decisions.

Melissa D McCradden

Division of Neurosurgery (McCradden, Baba, Saha, Boparai, Fadaiefard, Cusimano), St. Michael's Hospital, Unity Health Toronto; Dalla Lana School of Public Health (Cusimano), University of Toronto, Toronto, Ont. injuryprevention@smh.ca.
Kelly Thai

Department of Bioethics, The Hospital for Sick Children, Toronto, Ontario, Canada.
Azadeh Assadi

The Hospital for Sick Children, Toronto, Ontario, Canada.
Sana Tonekaboni

SickKids Research Institute, Toronto, Ontario, Canada.
Ian Stedman

School of Public Policy and Administration, York University, Toronto, Canada.
Shalmali Joshi

Vector Institute, Toronto, Ontario, Canada.
Minfan Zhang

Vector Institute for AI, Toronto, Ontario, Canada.
Fanny Chevalier

Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Anna Goldenberg

SickKids Research Institute, 686 Bay Street, Toronto, ON M5G 0A4, Canada; Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4, Canada. Electronic address: anna.goldenberg@utoronto.ca.

Artificial Intelligence Child Clinical Decision-Making Female Grounded Theory Hospitals, Pediatric Humans Interviews as Topic Machine Learning Pediatrics Qualitative Research

View on PubMed Access via DOI PubMed (39939160)