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.

LKCMedicine Dean's Blog 5 - TRUST: Can we trust the Machine? Can we trust the Doctor?

If we take this paradigm to consider patient-doctor relationship, we will find some analogy. The trust between patients and their doctors is absolutely crucial in management of health and disease conditions. When a patient presents himself/herself to a doctor with little idea of what is the problem with their body, he/she also puts his/her life in the hands of someone whom he/she has little knowledge. The tests that he/she has to go through, the interpretation of test results, the diagnosis, the options of treatment and the likely outcome of these treatment options are all unfamiliar to the patient. Therefore, what will be needed is an unwavering trust of the doctor, who is equipped with the latest medical knowledge; who knows exactly what he is doing; who is giving him/her the best advice (not for the consultation fees but for the best clinical outcome) for the condition. 

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. 

LKCMedicine Dean's Blog 5 (2) - TRUST: Can we trust the Machine? Can we trust the Doctor?

Therefore, as a medical student, you should learn how to listen to your patient, to explain to your clients, and keep yourself abreast of new knowledge and skills, so that the advice and treatment you offer will evolve with scientific advancement. 

With the advent of AI-assisted medicine, doctors have an additional role to play. On one hand, we need to learn to “trust” the machine that is smarter and faster than us, but knows when to exercise discretion of human intelligence. On the other hand we have to guide our patients to “trust” diagnosis, prognostication and management recommended by the machine. This can be pictured as we are sitting together with our patients in a driverless car. The car, driven by AI/ML, can react faster and more accurately, with no fatigue, unlike a human driver. Step by step, we have to learn that the AI-driven car is reliable and can take us to the destination safely and efficiently. We will learn how to “trust” this driverless car. But I will keep the steering wheel and the brake pedal at the driver’s seat, so that I can turn the wheel, stamp on the brake and take over the machine when needed. That would be the future of driving. That would be the future of medicine.