What a multi country randomized trial tells us about AI assisted clinical decision making
Homspital Clinical Insight | September 2026
Artificial intelligence in healthcare is often framed as a competition:
Can AI perform better than a doctor?
But a new randomized controlled trial suggests that the more important question may be different:
Can clinicians make better decisions when AI supports them?
A multi country randomized controlled trial published in npj Digital Medicine on 9 September 2026 evaluated 249 physicians across Kenya, Indonesia and the Netherlands.
Physicians were randomized to complete standardized clinical cases either independently or with access to a large language model, GPT4o.
Physicians with LLM assistance achieved significantly higher clinical performance scores in all three countries.
The improvement was greatest in Kenya, where LLM supported physicians scored 18 percentage points higher than physicians without LLM access. Improvements were also observed in Indonesia and the Netherlands.
What Changed?
The significance of this study is not that artificial intelligence can replace clinical professionals.
It is that AI may become more valuable when it is designed to augment clinical reasoning rather than substitute for it.
This changes the conversation from:
Clinician vs AI
to:
Clinician + AI
In this model, artificial intelligence can help organize information, provide additional perspectives and support clinical reasoning.
The final clinical decision, however, remains with the healthcare professional.
Why This Matters
Healthcare decisions rarely depend on a single piece of information.
Clinicians must combine symptoms, medical history, medications, investigations, physiological data and changing clinical circumstances.
As remote patient monitoring, continuous glucose monitoring, wearable medical devices and connected healthcare systems generate increasing volumes of information, clinicians may face an additional challenge:
too much data rather than too little.
AI may eventually help transform this information into structured clinical context.
But the value of AI should not be measured simply by whether it can generate an impressive recommendation.
The important question is whether AI helps healthcare professionals make safer, more appropriate and more timely decisions.
Important Limitations
The findings should be interpreted carefully.
This was a controlled study using standardized clinical cases not real world patient encounters.
The study did not evaluate patient outcomes or harms.
Physicians in the control group were also not permitted to use conventional external resources such as clinical guidelines or internet searches, which does not fully reflect routine clinical practice.
Therefore, the study does not demonstrate that LLM supported clinical decision making improves patient outcomes.
It also does not provide evidence for autonomous AI decision making.
What it provides is stronger evidence that human AI collaboration deserves further clinical evaluation.
Clinical Governance Implications
As AI becomes integrated into clinical workflows, healthcare organizations will need governance systems that clearly define:
- what the AI system is intended to do;
- which decisions it may support;
- where human review is mandatory;
- who remains accountable for the final clinical decision;
- how clinicians can challenge or override an AI recommendation;
- how AI supported decisions are documented and audited;
- how model performance and safety are monitored over time.
A technically powerful AI system without clear clinical accountability is not a safe clinical system.
Homspital Clinical Insight
The emerging direction of clinical AI is increasingly clear:
AI should strengthen clinical judgment not replace clinical accountability.
The next generation of healthcare AI research therefore needs to move beyond testing whether algorithms can answer medical questions.
The more important questions are:
- Does AI improve clinician performance in real clinical environments?
- Does it reduce errors?
- Does it improve patient outcomes?
- Can clinicians recognize when the AI is wrong?
- And can healthcare organizations maintain clear accountability when AI becomes part of the decision-making process?
Until those questions are answered with robust real world evidence, human oversight remains fundamental.
Evidence Classification
Evidence Type: Multi country randomized controlled trial
Evidence Quality: ★★★★ High
Clinical Relevance: High
Current Practice Impact: Supports further development of humansupervised AI assisted clinical decision support; does not support autonomous AI decision making.
Recommended Action: Monitor Closely / Develop Governed HumanSupervised Applications
Reference:
Rounding N, Arif LS, Berg J, et al. Impact of LLM assistance on physician decision making: a multi country randomized controlled trial. npj Digital Medicine. Published 9 September 2026.