Adapting the Machine to the Medicine: New Review Shows How to Make Clinical AI Work
Key Takeaways:
- A new review in the Journal of Medical Internet Research analysed 35 recent studies and found that off-the-shelf large language models must be adapted for medical settings before they can be relied upon for diagnosis, triage and treatment planning.
- Performance depends on the clinical task: retraining the model on task-specific data suited narrow jobs such as detecting cancer in medical images, while connecting the model to live, trusted databases worked well for reasoning through complex guidelines. Hybrid systems combining both performed best of all.
- Most of the evidence to date comes from historical medical records rather than live clinical use, so prospective, real-world testing remains essential before widespread hospital adoption.
Why raw capability is not enough
Generative artificial intelligence has arrived in health care with considerable fanfare, but a new review suggests that the decisive factor is not the sheer power of the underlying model. It is what happens after the model is built. A review study published in the Journal of Medical Internet Research by JMIR Publications concludes that adapting existing language models to the clinical environment is the key to ensuring they can safely and effectively support diagnosis, patient triage and treatment planning in real-world settings.
The research team, led by Anshum Patel, MD, and Joseph Y Cheung, MD, MS, analysed 35 recent studies to understand how different customisation methods affect AI performance. Their conclusion was that standard large language models (LLMs) are undoubtedly capable, but capability alone does not translate into clinical reliability. To be trusted in a consulting room or on a ward, a model has to be tailored specifically for the medical environment in which it will be used.
What adaptation actually looks like
The review examined the two broad routes clinicians and developers can take. The first is retraining, in which a general-purpose model is further trained on specific clinical data or guidelines so that it internalises the patterns and standards of a particular task. The second is connection, in which the model is linked directly to trusted medical databases and draws on that source material at the point of use rather than relying solely on what it absorbed during training.
Both approaches produced meaningful gains. When models were connected directly to trusted medical databases or retrained on specific clinical guidelines, accuracy greatly improved, with some systems matching the diagnostic performance of human doctors. That is a striking benchmark, and it underlines the central argument of the review: the adaptation step is not a technical afterthought but the point at which a general tool becomes a clinical one.
The right method depends on the task
The most successful approach was found to depend heavily on the specific medical task at hand, and this is arguably the review’s most practical finding for anyone evaluating these tools.
For narrow, focused tasks such as detecting cancer in medical images, retraining the AI on specific data worked best. Where the job is well defined and the data are consistent, teaching the model directly on that material produces the sharpest results.
For tasks that require reasoning through complex guidelines, linking the AI to live databases was highly effective. Clinical guidance changes, and a model that consults an authoritative, current source is far better placed than one working from a fixed snapshot of its training data.
However, the researchers determined that the best performance came from hybrid systems, which combine both methods to manage complicated workflows such as stroke triage and oncology cases. These are precisely the situations in which speed, protocol adherence and nuanced judgement all matter at once, and where people presenting with time-critical or complex conditions stand to gain the most from well-designed decision support.
“There is no single best way to adapt AI for health care. The right approach depends on the clinical task, and the next step is making sure these systems are safe, reliable, and useful in real-world patient care,” says Anshum Patel.
Implications for clinical teams
For healthcare professionals, the message is not that they need to become engineers. It is that the questions worth asking about any AI tool offered to a service are increasingly practical ones: what was this model adapted for, what source material informs its outputs, how current is that material, and has it been tested on a population and a workflow resembling ours?
That kind of informed scrutiny is becoming part of everyday clinical literacy, and it is one reason structured professional development in this area has grown in demand. The College of Contemporary Health’s AI Essentials for Primary Care short course is designed for exactly this purpose, helping clinicians understand how these systems are built, where their limitations lie, and how to appraise them responsibly within their own practice.
The evidence gap that still needs closing
While these findings are promising, the researchers note that the vast majority of studies on these AI systems are based on past medical records rather than live patient testing. Retrospective analysis can demonstrate that a system performs well against a tidy historical dataset; it cannot show how that system behaves when confronted with incomplete notes, atypical presentations, or the ordinary time pressures of a busy department.
Before these advanced tools are widely adopted in hospitals, additional prospective, real-world testing is needed to guarantee patient safety and ensure the technology works reliably across different clinical environments. Until that evidence accumulates, the sensible position is one of informed optimism – recognising the genuine potential of adapted clinical AI while insisting that it earns its place through testing in the settings where people actually receive care.
CCH insight
Artificial intelligence is moving quickly from conference agendas into everyday clinical workflows, and the professionals best placed to use it well are those who understand both its strengths and its blind spots. AI Essentials for Primary Care is a CPD-accredited short course from The College of Contemporary Health, built for clinicians who want a clear, practical grounding in how AI tools work, how to evaluate them critically, and how to apply them safely in patient-facing practice.
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Source: Journal of Medical Internet Research
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AI Is Now the World’s Top Healthcare IT Investment, Report Finds
Key Takeaways:
- Artificial intelligence is now the leading healthcare IT investment priority across most major markets outside the United States, named by 57% of organisations surveyed.
- Ambient AI dominates clinical interest, with third-party tools such as Microsoft Dragon Copilot commanding the widest cross-regional appeal.
- Cloud adoption is maturing from planning to deployment, with nearly three-quarters of organisations now using the cloud in some capacity.
AI moves to the front of the queue
Interest in artificial intelligence continues to surge, and it has now risen to the top of the priority list for healthcare organisations across most major markets outside the United States. According to a new report from KLAS Research, the share of organisations naming AI as a strategic initiative climbed by 15 percentage points year on year – a striking marker of how quickly the technology has moved from experiment to strategic imperative.
The fourth annual KLAS Research report gathered insights from 182 healthcare organisations spanning 43 countries and territories outside the United States. Artificial intelligence emerged as the single most cited healthcare IT investment priority, named by 57% of respondents. It was followed by electronic health record (EHR) and digitalisation work at 44%, IT and infrastructure at 26%, cybersecurity at 25% and imaging at 23%.
Ambient AI captures the clinical imagination
When respondents were asked which technologies most excited them, ambient AI stood out clearly. A matching 57% pointed to ambient AI as the most “exciting emerging technologies,” ahead of other clinical AI at 17%, operational AI at 16% and general or unspecified AI at 16%.
Governance is keeping pace with enthusiasm. AI strategy, governance and adoption ranked as the leading priority in every region at 37%, followed by clinical documentation at 19%, imaging at 13%, AI platforms and integration at 11%, advanced capabilities at 10%, clinical decision support at 4%, operational efficiency at 3% and patient engagement and experience at 3%.
Among the ambient AI offerings drawing the most attention, the majority are supplied by third-party vendors. Microsoft Dragon Copilot continues to hold the highest cross-regional mindshare, while interest in Heidi Health has grown across Europe and Oceania over the past year. A number of regional players are also in the running, with Augnito attracting interest in the Middle East and TORTUS gaining ground in the UK.
As ambient documentation and clinical AI move from pilot projects into everyday practice, primary care professionals are increasingly expected to understand not only how these tools function, but when their use is appropriate, safe and ethical – a skills gap that structured continuing professional development is beginning to address.
Cloud strategies mature from planning to practice
Cloud adoption is accelerating as organisations shift from planning towards deployment. Nearly three-quarters of respondents now draw on the cloud in some capacity as part of their IT strategy. While only 26% report having moved a core system – such as an EHR, PACS or ERP – into the cloud, organisations worldwide are increasingly operationalising their cloud plans and moving beyond evaluation towards meaningful adoption, according to KLAS data.
Electronic health records sit at the centre of these cloud strategies. More than a third of cloud users – particularly across the Middle East and Canada – have moved all or part of their EHR to the cloud, while nearly a quarter have partially or fully migrated imaging applications such as PACS.
Artificial intelligence is itself shaping how organisations approach the cloud. Those earliest in their cloud journey most often report either using specific AI tools within the cloud or moving their data infrastructure into cloud environments to unlock stronger analytics and computing capabilities.
Electronic health records anchor digital investment
For the organisations reporting EHR and digitalisation investments, nearly a third are either actively implementing an EHR or weighing up replacements or consolidations – chiefly across Europe, the Middle East and Oceania, the report notes.
“Those not involved in such projects are largely focused on increasing digital maturity and optimizing existing EHRs to improve workflows and strengthen the foundations needed for AI and analytics, which is a consistent priority across regions; Latin America in particular has a focus on increasing digital maturity,” the report said.
Consulting demand cools as purchasing slows
Alongside the enthusiasm for AI, analysts observed a cooling in appetite for outside help. Global interest in consulting firms declined between 2025 and 2026, with 42% of organisations reporting no plans to engage such firms and only 25% citing “concrete plans” to bring in consultants for upcoming IT initiatives. The report also noted that 2025 saw the lowest level of EHR purchasing activity in years.
CCH insight
As ambient AI and clinical documentation tools become part of routine care, the professionals using them need to know how to apply AI safely, ethically and effectively at the point of care. The College of Contemporary Health’s CPD-accredited short course, AI Essentials for Primary Care: Tools, Ethics and Everyday Applications (3.5 CPD points, 3.5 hours), is designed to help clinicians do exactly that.
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