Conflicting roles for humans in learning health systems and AI-enabled healthcare.
T J KasperbauerPublished in: Journal of evaluation in clinical practice (2020)
The goals of learning health systems (LHS) and of AI in medicine overlap in many respects. Both require significant improvements in data sharing and IT infrastructure, aim to provide more personalized care for patients, and strive to break down traditional barriers between research and care. However, the defining features of LHS and AI diverge when it comes to the people involved in medicine, both patients and providers. LHS aim to enhance physician-patient relationships while developments in AI emphasize a physicianless experience. LHS also encourage better coordination of specialists across the health system, but AI aims to replace many specialists with technology and algorithms. This paper argues that these points of conflict may require a reconsideration of the role of humans in medical decision making. Although it is currently unclear to what extent machines will replace humans in healthcare, the parallel development of LHS and AI raises important questions about the exact role for humans within AI-enabled healthcare.
Keyphrases
- healthcare
- artificial intelligence
- end stage renal disease
- newly diagnosed
- ejection fraction
- machine learning
- chronic kidney disease
- prognostic factors
- emergency department
- peritoneal dialysis
- deep learning
- quality improvement
- health information
- public health
- social media
- primary care
- case report
- density functional theory
- data analysis
- chronic pain
- health insurance