New York: While artificial intelligence systems like ChatGPT are making their way into everyday use, doctors are less likely to adopt them over lack of skills to interpret and act according to it, finds a study.
Like every other industry, physicians will soon start to see AI tools incorporated into their clinical practice to help them make important decisions on diagnosis and treatment of common medical conditions.
These tools, called clinical decision support (CDS) algorithms, can be enormously helpful in helping guide health care providers in determining, for example, which antibiotics to prescribe or whether to recommend a risky heart surgery.
The success of these new technologies, however, depends largely on how physicians interpret and act upon a tool’s risk predictions -- and that requires a unique set of skills that many are currently lacking, according to a new perspective article published in the New England Journal of Medicine.
CDS algorithms, which make predictions under conditions of clinical uncertainty, can include everything from regression-derived risk calculators to sophisticated machine learning and artificial intelligence-based systems. They can be used to predict which patients are most likely to go into life-threatening sepsis from an uncontrolled infection or which therapy has the highest probability of preventing sudden death in an individual heart disease patient.
“These new technologies have the potential to significantly impact patient care, but doctors need to first learn how machines think and work before they can incorporate algorithms into their medical practice,” said Daniel Morgan, Professor of Epidemiology & Public Health at University of Maryland School of Medicine (UMSOM), and co-author of the perspective.
While some clinical decision support tools are already incorporated into electronic medical record systems, health care providers often find the current software to be cumbersome and difficult to use.
“Doctors don’t need to be math or computer experts, but they do need to have a baseline understanding of what an algorithm does in terms of probability and risk adjustment, but most have never been trained in those skills,” said Katherine Goodman, Assistant Professor of Epidemiology & Public Health at UMSOM and co-author of the perspective.
To address this gap, medical education and clinical training need to incorporate explicit coverage of probabilistic reasoning tailored specifically to CDS algorithms, the authors suggest.
They also proposed that probabilistic skills should be learnt early in medical schools, physicians should be taught to critically evaluate and use CDS predictions in their clinical decision-making, practise interpreting CDS predictions.
They should also learn to communicate with patients about CDS-guided decision making.