Published on 04 Jan 2022
TRUST: Can we trust the Machine? Can we trust the Doctor?
Professor Joseph Sung
Dean, Lee Kong Chian School of Medicine
Artificial intelligence (AI) and Machine Learning (ML) are permeating every aspect of our life, including medicine. ML can assist in reading radiological, endoscopic and histological pictures, suggesting diagnosis, recommending therapy and surgical decisions and even predicting outcome. However, the application of AI and ML tools in medicine is slow compared to many other industries. One of the biggest challenges of utilising artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a “black box”: machine prognostication, and prediction of outcome without offering much information of what is the rationale behind all these vectors. In this black box, what “goes in” and what “comes out” are not necessarily rationalisable. Besides clinician’s doubt, patients lacking confidence with AI-powered technologies also hampers development. While they may accept the reality that human errors can occur, there is little tolerance for machine error. In order to implement AI-assisted medicine successfully, interpretability of ML algorithm needs to improve. Opening the black box in AI medicine needs to take a stepwise approach. Small steps of biological experimentation and clinical experience in ML algorithm can help to build trust and acceptance. AI software developers will have to clearly demonstrate that when the ML technologies are integrated into the clinical decision-making process, they can actually improve clinical outcome. Enhancing interpretability and continuous improvement in accuracy and efficacy of ML algorithm is crucial.
Studies have shown that one of the most important factors determining the outcome of clinical management is good rapport between patients and doctors. The patients trust their doctors for their knowledge, skills and professionalism. The doctors trust that their patients are giving them honest and complete information about their symptoms, social and psychological conditions. And the patient, with their attending doctor, walk through the journey together. That is an ideal doctor-patient relationship. In order to build this trust, the clinicians need to listen carefully what is behind the symptoms and complaints of the patients, explain in simple and clear terms of the medical condition, and provide continuous adjustment and improvement on the treatment to their patients. Just like AI/ML algorithm, clinical management is not a straightforward “one question, one answer” process. Rather, it is a loop of trusting, understanding and improving, leading to a favourable outcome.