Generative AI for family medicine: friend or foe?

Carmen Wong 黃嘉雯

HK Pract 2023;45:33-34

ChatGPT is the talk of the town, so what is all the fuss? If you are an ‘early adopter’ you are likely already experimenting prompts whilst the ‘laggards’ amongst us are voicing suspicion and caution. Many of us like somewhere in the middle.¹ Generative artificial intelligence (AI) develops algorithms in creating new content by piecing together relevant data, in this case text and ChatGPT is a natural language processing tool driven by generative AI.² The result is human like communication in responding to queries and the ability to complete tasks with simple prompts. The breadth of its language abilities is impressive and can configure responses to different styles or expression almost immediately. The speed in writing emails, explaining concepts and writing essays takes seconds rather than the usual minutes or hours. Since its launch in November 2022, it has transformed our thinking about the potential of generative AI but also raises serious questions on how humans make sense of the processing power which far outperforms us on the vast amount of information out there.

In the educational field the utilisation is in leaps and bounds. Teachers are utilising ChatGPT responses to inform course outlines, learning outcomes, assessment rubrics. Initial resistance has yielded to optimistic possibilities in partnering with the progressively evolving technology and in seeing AI as students’ study buddy offering guidance and direction in ambiguous spaces and for teachers as curriculum co-designers.

Surprisingly generative AI is in fact not new, but the explosion of interest was the ability to connect to users directly. As a physician, you are probably familiar with the ability of algorithms in analysing images from X-rays, MRIs and retinographs to aid diagnosis. We have often been in situations in which we ridiculed erroneous comments on ECGs whilst being reassuringly confident when diagnoses are aligned. As generative AI develops in handling images, the value of a tool which can scrutinise the microscopic features of images coupled with the experience of correct and mis-diagnosis from a thousand experts, or can summarise the evidence base (and disparities) relevant to our queries at the tips of our fingers. This can potentially change our practice and health care systems. Think further in how a tool could draft patient advice and information to different educational levels on different concerns, personalised care could be delivered more effectively. Imagine how a tool in summarising all the desired key points and observations across previous consultations for writing medical report or referral letters could help you save time. Time is our most valuable asset. Staying up to date can be easy as the ability to summarise the latest guidelines and recommendations on a particular topic needs only seconds. ChatGPT has shown the potential in performing United States Medical Licensing Exams (USMLE)³ and Stanford is developing BioMedLM (previously known as PubMedGPT) AI training on biomedical abstracts and papers.⁴

Personally, I am relieved that generative AI is on the horizon in this age of information. Back in the early days of my medical studies (1996), the main source of information were textbooks and lecturers’ notes. Journal articles were available in libraries or on request. The swell of information on the internet after graduation had been somewhat manageable by search engines e.g. google and medical databases e.g. dynamed etc. However, as I slowly grew accustomed to the avalanche of information flooding family medicine, I became increasingly concerned of how to stay afloat. Thus, AI, to me is a much needed friend or rather a handy personal assistant working quietly, sifting, sorting and recalling information just as I need it.

Things are rarely so ideal and we are right to be cautious as there are several issues to attend to. The primary concern is trust. No system is ever fail-safe even when fed with the right input and extensively trained and tested, errors exist. Since the public testing of ChatGPT, there has been many incidences of ‘hallucinations’ in which responses sound plausible but are factually incorrect or unrelated.3 There will of course, be implications for our professional realm in both legal and ethical issues as generative AI has trouble deciphering context, nuances and prejudices. Managing complexity is still what humans do best. Instinctively we know this, as we tackle complexities in family medicine daily: explaining diagnoses to those with poor understanding of their medical conditions, in managing multimorbidity and in navigating hidden agendas.

I remain hopeful that AI may reduce the tedium of administrative work and salvage precious minutes in patient interaction or in chasing the elusive work life balance for our own health. My verdict: Generative AI is here to stay, perhaps too early for a warm embrace but not too late for a firm handshake.

This editorial has been written without the use of AI generated text or content.

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