AI for medical diagnosis: what clinicians find most helpful

Nov 11, 2026 | 3 minute read

By the Philips Editorial Team

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At a glance

Used right, AI for medical diagnosis can help clinicians make decisions faster and with greater confidence.
For example, AI can help analyze scans, bring patient information together and flag potential anomalies that require closer inspection to support early detection.
Yet while clinicians value AI as a tool for clinical decision support, most of them believe that human oversight remains critical, and that AI performance needs to be carefully monitored over time.

Clinicians are already using AI across the diagnostic process, from analyzing medical images such as MR and CT scans to bringing together different sources of patient data to develop more personalized treatment plans. But as AI becomes more embedded in clinical practice, how do clinicians feel about its role, and what do they expect from it?

A woman lies on a medical imaging table as a clinician positions her head beneath a scanner

The 2026 Philips Future Health Index set out to answer these questions, gathering perspectives from 2,000 healthcare professionals across 10 countries. The report reveals their expectations of AI, where they are already seeing an impact on patient care, but also the concerns they have about AI as its adoption continues to grow.

Here are five findings that stood out:

1. AI can provide a second set of eyes

By analyzing vast volumes of medical imaging data, AI tools can help surface relevant patterns that might otherwise go unnoticed, serving as a second set of eyes for clinicians. Thirty-nine percent of healthcare professionals in the 2026 Philips Future Health Index say AI has helped them identify or prevent a potential medical error at least three times in the previous three months, potentially including missed or delayed diagnosis.

In lung cancer screening, for example, where timely detection is essential, AI-assisted analysis can help radiologists identify lung nodules 26% faster and detect 29% of previously missed nodules. For patients, that could mean the difference between early detection, when their lung cancer may still be treatable, and a diagnosis after it has already progressed and the prospect of recovery is smaller.

2. AI can support faster, more confident diagnostic decision-making

The 2026 Philips Future Health Index shows that AI-enabled time savings extend to other areas of medical diagnosis as well. Among clinicians who use AI, 67% say it has improved the speed of diagnostic decision-making. Nearly as many (65%) report greater confidence in their decisions, while 63% say AI has helped them receive diagnostic results faster.

Some of these time savings may stem from automating routine tasks. Others come from being able to access relevant data more quickly. Diagnostic information is often spread across different systems, burdening clinicians to piece together the full patient picture. In last year’s Future Health Index, more than three in four healthcare professionals reported losing time because patient data was incomplete or inaccessible. This year, 57% of clinicians say AI has improved their access to consolidated patient data across care teams.

By bringing together different sources of patient information, AI can support integrated diagnostics, connecting insights from radiology, pathology and other disciplines to help create a more complete picture of each patient.

3. AI is expanding access and capacity

Increased workflow efficiency and faster diagnostic decision-making can also help care teams serve more people. Half of clinicians in the 2026 Future Health Index say AI has increased their capacity to see more patients.

Case in point: at one hospital in India, AI-enabled CT workflow tools helped the radiology department increase its daily scan volume from 70-80 to 90-100 exams. Referring clinicians received images and reports sooner, helping them make more timely decisions about patient care. Similarly, at an imaging center in Germany, AI-based MRI acceleration software helped increase patient throughput by about 20%.

AI may also help extend the reach of patient care to those who currently struggle to access it. Seventy-five percent of clinicians say AI can help narrow quality gaps between healthcare settings and improve services in underserved and rural areas. For example, in rural Kenya, an AI-enabled application for handheld ultrasound is being piloted to help midwives and nurses assess pregnancies and identify when additional care may be needed. This could bring pregnancy screening and referral support closer to women who live far from clinics.

4. Trust requires transparency and ongoing monitoring

As clinicians weigh AI’s role in medical diagnosis, they expect insight into how it reaches its recommendations and whether it performs reliably across different settings. That’s where many still have concerns: 66% of clinicians in the 2026 Future Health Index cite the possibility of AI errors, while 60% point to a lack of transparency. Another 58% are worried about inconsistent performance across clinical settings, as algorithms trained on one patient data set may not readily transfer to patients in another setting.

Clinicians also see gaps in how AI is monitored over time. Seventy percent say clear processes for monitoring AI-enabled tools are not yet in place. As AI continues to advance, safeguards against algorithmic drift – a gradual decline in AI’s accuracy – will be particularly important to help ensure that AI improves diagnostic decisions reliably and consistently.

5. Clinicians want AI to support, not to decide for them

The fact that clinicians still have concerns about AI helps explain why they want to remain firmly in control of diagnostic decisions. When asked about diagnostic decision-support tools, 88% of clinicians in the 2026 Future Health Index say they are comfortable with AI supporting decisions as a partner. But just 3% are comfortable with AI deciding and acting independently. The pattern is similar for medical image analysis: 86% of clinicians are comfortable with AI support, while only 5% are comfortable with AI deciding and acting independently.

These findings suggest that clinicians value AI as an aid to their judgment, but that they are wary of relinquishing control over diagnostic decisions. In fact, 90% of clinicians say that keeping a human in the loop is essential, and 86% say all AI outputs require human oversight. Clinicians are finding that the greatest value comes from combining AI capabilities and human expertise, pointing to a more collaborative model of care in which each strengthens the other.

In the hybrid care team of the future, the careful diagnostic judgment of a clinician – honed over years of experience – will still be critical, but AI could help them apply it even more effectively, so more patients can benefit from it.

For more insights into how clinicians are adopting AI, download the 2026 Philips Future Health Index global report.