{"id":4771,"date":"2026-04-02T09:27:52","date_gmt":"2026-04-02T08:27:52","guid":{"rendered":"https:\/\/blogs.bmj.com\/medical-ethics\/?p=4771"},"modified":"2026-04-02T09:27:52","modified_gmt":"2026-04-02T08:27:52","slug":"dont-let-ai-steal-your-first-thought","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/medical-ethics\/2026\/04\/02\/dont-let-ai-steal-your-first-thought\/","title":{"rendered":"Don\u2019t let AI steal your first thought"},"content":{"rendered":"

By Nguyen Phu Nghia<\/p>\n

In January 2026, Utah (USA) announced a first-in-the-nation partnership allowing an AI system to support prescription medication renewals for patients<\/a>, moving beyond suggestion and into direct participation in clinical workflow. Similarly, Mass General Brigham has implemented an AI-supported primary care program<\/a> in which an AI agent collects patient histories, generates preliminary diagnostic and treatment suggestions, and forwards structured summaries to physicians, enabling rapid telehealth consultations. At the same time, public trust in AI tools<\/a> appears to be growing rapidly: many members of the public are increasingly turning to AI for health advice, sometimes placing considerable confidence in its recommendations. AI is being applied with remarkable breadth across healthcare, offering not only automation of administrative tasks but also new capabilities in clinical reasoning support.<\/p>\n

At the individual level, this shift is happening just as quickly. AI is predicted to transform clinical workflows in 2026<\/a> by reducing the time clinicians spend gathering and synthesising data, helping to organise evidence, and suggesting possible pathways for care. Specialised differential diagnosis tools<\/a> are already being developed to assist clinicians in generating and prioritising lists of possible diagnoses, in effect influencing early reasoning steps by broadening consideration of potential conditions. These applications can help clinicians surface a wider set of options or reduce time hunting for data, but they also raise questions about where and when human judgement should play the decisive role.<\/p>\n

A trainee doctor sits at a computer, scrolling through a new patient\u2019s electronic record. The story does not line up neatly. The symptoms blur into one another. There is no obvious answer waiting to be pulled from a textbook. For a moment, they hesitate. Before writing a single line of assessment, they open an AI tool and type, \u201cMost likely differential diagnosis?\u201d Within seconds, a polished list of differential diagnoses appears, even outlining what tests might help confirm the final diagnosis and what should be done next.<\/p>\n

Are those answers correct? Perhaps.<\/p>\n

When are they wrong? And who will recognise it?<\/p>\n

The risk is not simply that AI may be inaccurate. The deeper risk is that it thought first and that its reasoning depends entirely on the information we chose to enter. In a nationwide study<\/a> in the United States analysing 42,579,676 emergency department visits for chest pain, only 5.1% were ultimately diagnosed with acute coronary syndrome (ACS), while more than half were found to have non-cardiac causes. When a doctor first meets a patient with chest pain, the first task is to hold multiple possibilities in mind, and construct a reasoned initial frame for the problem.<\/p>\n

That first attempt matters more than we sometimes realise. It is when a clinician begins to take ownership of a case and judgment starts to form. If that step is skipped, learners shift from building explanations to supervising outputs, from creating to comparing. It may feel efficient, but over time the difference accumulates. If AI generates the first differential every time, trainees are not practising how to create one, and without that practice the skill may never fully develop. This is never-skilling: a competence that never truly takes root because something else always goes first. It is like learning to drive with autopilot constantly on. When it switches off, you would not want that to be your first real attempt.<\/p>\n

Clinical work carries similar demands. Concerns about errors and unintended consequences have been raised as AI systems enter high-stakes environments<\/a> such as the operating room. Junior clinicians, especially early in training, may not yet have the depth of experience to detect contextual gaps or flawed assumptions. If their own reasoning skills are underdeveloped, oversight becomes fragile.<\/p>\n

So what should we be doing?<\/a><\/p>\n

Clinical reasoning should begin with independent human judgment. AI can then challenge, expand, or stress-test that reasoning. But the process must return to the clinician for contextual interpretation, accountability, and responsibility. We are not preparing doctors to compete with machines. We are preparing them to work alongside tools that are powerful, imperfect, and context-blind. Their value will depend on clinicians who can question them, adapt them, and, when necessary, override them.<\/p>\n

If we allow AI to supply the first thought every time, we risk raising a generation of clinicians who are fluent in editing but less practised in reasoning from scratch. The first thought in clinical care is usually awkward, partial, sometimes wrong, and almost always revised. It may feel uncomfortable to commit to it. It takes time and carries doubt. Yet it is in that imperfect beginning that judgment slowly develops. It is where responsibility begins. And we should not let AI take that away.<\/p>\n

Paper Title<\/strong>: How can junior doctors safeguard their development as clinicians in the age of AI?<\/a><\/p>\n

Authors<\/strong>: Nghia Phu Nguyen1<\/sup>, Van-Phu Tran1<\/sup>, Ai Cao My Bui2,3<\/sup>, Phillip Tran1,4<\/sup>, Hoang Nguyen1,5<\/sup><\/p>\n

Affiliations<\/strong>: 1<\/sup>College of Health Sciences, Nam Can Tho University, Can Tho City, Vietnam; 2<\/sup>School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam; 3<\/sup>Outpatient and Cardiovascular Imaging Department, University Medical Center HCMC, Ho Chi Minh City, Vietnam; 4<\/sup>Cardiovascular Department, Yavapai Regional Medical Center, Arizona, USA; 5<\/sup>School of Medicine, University of California at Los Angeles\/Charles R. Drew (UCLA), California, USA<\/p>\n

Competing interests<\/strong>: None to declare<\/p>\n

Social Media<\/strong>:<\/span>\u00a0<\/span>Nghia Phu Nguyen Linkedln<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

By Nguyen Phu Nghia In January 2026, Utah (USA) announced a first-in-the-nation partnership allowing an AI system to support prescription medication renewals for patients, moving beyond suggestion and into direct participation in clinical workflow. Similarly, Mass General Brigham has implemented an AI-supported primary care program in which an AI agent collects patient histories, generates preliminary […]<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":522,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8068],"tags":[],"class_list":["post-4771","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence"],"yoast_head":"\nDon\u2019t let AI steal your first thought - Journal of Medical Ethics blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blogs.bmj.com\/medical-ethics\/2026\/04\/02\/dont-let-ai-steal-your-first-thought\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Don\u2019t let AI steal your first thought - Journal of Medical Ethics blog\" \/>\n<meta property=\"og:description\" content=\"By Nguyen Phu Nghia In January 2026, Utah (USA) announced a first-in-the-nation partnership allowing an AI system to support prescription medication renewals for patients, moving beyond suggestion and into direct participation in clinical workflow. 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