Saturday, 5 September 2026
Artificial intelligence has moved rapidly into everyday medical practice, but healthcare’s next challenge may be considerably harder than getting doctors to use the technology. As AI begins moving beyond documentation and administrative support towards diagnosis, clinical decision support and personalised treatment, physicians and medical schools increasingly need to determine which capabilities can safely be delegated to machines and which skills doctors must continue to develop themselves.
Speaking at Ai4 2026 in Las Vegas, American Medical Association CEO John Whyte said the profession has barely begun to use the full potential of AI. The AMA’s latest physician survey found that more than four in five doctors reported using some form of AI professionally in 2026, more than double the proportion recorded when the organisation began surveying physicians in 2023. Much of today’s activity remains concentrated around functions such as summarising medical information and preparing clinical documentation, but Whyte argued that the larger opportunity lies in identifying patterns, supporting earlier diagnosis and helping clinicians develop more personalised treatment strategies.
The AMA deliberately uses the term augmented intelligence to emphasise what it sees as the appropriate relationship between medicine and technology. AI should improve a physician’s ability to make decisions rather than become an autonomous replacement for clinical judgement. That distinction becomes more important as systems become better at interpreting complex information. Imaging is an obvious example, where algorithms can assist specialists in detecting abnormalities, prioritising cases and identifying patterns that might otherwise be missed. Rather than eliminating radiologists or other specialists, the more likely near-term effect is a redistribution of work in which machines handle more repetitive analysis while physicians concentrate on difficult findings, clinical context and decisions requiring judgement.
This development also challenges one of the traditional foundations of medical education. For generations, doctors were expected to accumulate and retain enormous amounts of information, while AI can now retrieve much of that knowledge almost instantly. The question therefore becomes whether tomorrow’s physicians should be trained primarily to remember information or to interrogate, interpret and challenge the information presented to them. Whyte argued that medical education cannot simply introduce AI from the first day of training without considering what students might fail to learn as a result.
If a student has always relied on an automated system to construct a patient history, suggest a differential diagnosis or interpret a physical finding, that physician may never develop the underlying skill independently. The danger is not necessarily that AI supplies poor information every time, but that doctors become unable to recognise the occasions when the system is wrong. Generative models can produce convincing answers even when the underlying conclusion is incomplete or incorrect, making it essential that medical professionals understand where recommendations came from, assess the quality of the evidence and recognise when a result conflicts with clinical experience or other information about the patient.
Medical schools are consequently beginning to rethink how AI fits into training rather than treating it as another software tool. The AMA has supported initiatives exploring how technology can personalise medical education while preserving the competencies doctors need in clinical practice. The challenge is similar to the arrival of calculators in mathematics: tools can remove repetitive work and improve productivity, but students still need to understand the underlying principles before they can judge whether the answer produced by the technology makes sense.
The same problem is appearing outside hospitals as consumers acquire unprecedented amounts of health information directly from technology. Watches, rings and other connected devices can now provide users with measurements related to sleep, heart rate, oxygen saturation, exercise and numerous other physiological indicators. This represents a significant change from the first generation of consumer trackers that primarily counted steps, but the availability of more data does not necessarily mean patients know what to do with it.
Whyte has warned that the distinction between a wellness product and a medical product can become increasingly difficult for consumers to understand when a device presents information that appears to indicate disease risk. Manufacturers may carefully describe a feature as an indicator or wellness measurement rather than a diagnostic tool, while users may nevertheless change their behaviour because of what the device reports. The regulatory framework is therefore becoming an increasingly important part of the AI and digital-health debate, particularly as consumer technology begins influencing decisions that previously depended on conventional medical testing.
Blood-pressure monitoring illustrates the issue. Continuous or frequent measurements could potentially improve cardiovascular care because physicians would gain a much richer picture than a reading taken during an occasional medical appointment. But that value depends on the reliability of the technology and on users understanding what the results mean. A patient who receives no warning from a consumer device might incorrectly assume that hypertension has been ruled out, potentially delaying conventional measurement or medical advice.
The problem therefore extends beyond whether a wearable produces a number. Healthcare needs to determine whether the number is sufficiently accurate, whether it is clinically meaningful, how it should be interpreted and what action the patient should take. As consumer health technology becomes more sophisticated, the separation between wellness information and clinical information is likely to become increasingly difficult to maintain. There is also the risk of turning health into a continuous monitoring exercise, with consumers checking sleep scores, recovery measures and other indicators throughout the day even when those numbers may not materially improve medical outcomes.
The issue of access creates another complication. Digital technology is often presented as a way of democratising healthcare by bringing expertise to patients regardless of location, but Whyte questioned whether that promise has yet been demonstrated consistently. Telemedicine and AI can reduce geographic barriers, but digital services can also become additional conveniences for people who already have strong access to healthcare. Insurance, affordability, broadband connectivity, digital literacy and the availability of clinicians still influence whether technology meaningfully expands access.
This means healthcare AI should not be evaluated only by the sophistication of the model. A diagnostic assistant that performs well technically but reaches mainly patients who already have extensive access to care may produce less social benefit than expected. The more meaningful question is whether technology changes outcomes for populations that currently struggle to obtain timely medical advice.
Administrative work remains one of the clearest immediate opportunities. Physicians spend significant time documenting care, completing forms, responding to messages and satisfying reporting requirements. AI can potentially absorb parts of this burden, allowing doctors to devote more attention to patients and complex cases. In this area, the risk of replacing clinical judgement is relatively limited while the potential productivity benefit is substantial.
Whyte’s broader argument, however, is that healthcare should resist allowing technology companies to determine medicine’s priorities simply because a particular tool has become technically possible. The industry should first define the clinical problem it wants to solve and then determine whether AI is the appropriate solution. Starting with technology and searching afterwards for a medical application risks creating impressive products without meaningful improvements in patient care.
That philosophy could increasingly shape how healthcare organisations invest in AI. The winners may not be hospitals or medical groups that deploy the greatest number of models. They may be those that identify where technology genuinely improves diagnosis, reduces administrative burden, expands access or strengthens the relationship between clinicians and patients while preserving accountability.
Medicine’s AI transformation therefore presents a different challenge from many other industries. In banking, logistics or corporate operations, automation can often be judged primarily through productivity, cost and speed. In healthcare, the consequences of an incorrect decision can involve a patient’s health or life. Efficiency matters, but it cannot become the only measure of success.
The question facing medicine is therefore no longer whether physicians will use artificial intelligence. Most already do. The more consequential question is how far that relationship should develop and what doctors must continue to know and decide for themselves. AI may ultimately allow physicians to practise with access to more knowledge, better pattern recognition and less administrative work than any previous generation, but the technology will create the greatest value only if healthcare preserves the ability of clinicians to question the machine rather than simply follow it. The future of medical AI may therefore depend as much on strengthening human judgement as on improving artificial intelligence.
Source: CIJ.World Research & Analysis Team
