Clinical AI tool adoption is rapidly growing

Over the past three years, AI has shifted from experimental technology to deeply embedded capability. It is now actively shaping the delivery, efficiency, and experience of healthcare, with both patients and clinicians getting increasingly acclimated to – and increasingly expectant of – AI in their interconnected workflows.

Clinicians and their patients are all using AI frequently in their personal and professional lives. Outside of the office, doctors, nurses, and patients are using AI at similar rates. More than eight out of ten (82%) doctors, 76% of nurses, and 81% of patients engage with AI tools at least once a week.

AI use within the clinical setting is only slightly lower, as 74% of doctors and 70% of nurses use AI at least once a week for professional purposes. But the numbers are still significantly higher than in 2025, when just 38% of doctors and 46% of nurses used AI tools at work once a week or more.

As AI is increasingly embedded across health ecosystems, it is profoundly shaping expectations and behaviors for how care is delivered and experienced.

How frequently do you use GenAI at work?

Stacked bar chart comparing frequency of generative AI use among total respondents, nurses, and doctors, with daily use as the most common across all groups.

  • Graphic description

    The image is a stacked vertical bar chart titled “How frequently do you use GenAI at work?” comparing three groups—Total, Nurses, and Doctors—each represented by a bar divided into five color-coded usage frequencies: once or more per day (dark green), once or more per week (light green), once or more per month (light blue), a few times per year (medium blue), and never (dark blue). In the Total group, 48% report daily use, 24% weekly, 9% monthly, 5% a few times per year, and 13% never; among Nurses, 44% report daily use, 26% weekly, 8% monthly, 5% a few times per year, and 18% never; among Doctors, 52% report daily use, 22% weekly, 10% monthly, 4% a few times per year, and 9% never. Across all groups, daily use is the largest segment, weekly use is the second largest, and monthly and yearly usage are smaller portions, while the percentage who never use GenAI is highest among Nurses and lowest among Doctors.

AI use in clinical practice has reached a daily habit stage

The intensity of use has also increased markedly. The share of clinicians using AI multiple times per day has more than tripled for doctors (from 10% in 2025 to 38% in 2026) and doubled for nurses (from 16% to 32%), indicating that AI is becoming embedded in dayto- day workflows.

The seismic nature of this shift gets even clearer when zeroing in on the percentages of clinicians who say they have never used AI in a professional context. In 2025, 43% of doctors and 34% of nurses said they had no experience with AI at work.

Just 12 months later, those “never used” numbers have collapsed. In 2026, a mere 9% of doctors and 18% of nurses never use AI tools in the workplace, showing that the technology has clearly crossed a threshold from digital novelty to routine workflow companion, particularly among the physician community.

Clinicians are using AI within the workflow for a variety of purposes, anchored by familiar use cases such as summarizing medical literature or analyzing data, generating patient education materials, checking drug interactions, using AI scribes for generating documentation, and supporting diagnostic discovery.

How patients are using AI to explore symptoms, diagnoses, and care options

This same pattern is emerging on the patient side as well. Patients are starting to view AI use as table stakes when it comes to their healthcare needs. Now that more than 7 in 10 patients use AI in their personal lives multiple times per week, health-related queries are taking center stage.

Just over half of patients are using AI to research side effects (54%) or ask for more information about diagnoses (52%), while 44% use AI to learn more about their symptoms. In addition, about 4 in 10 patients are or would consider using AI to simplify complicated medical jargon and interpret test results.

Unlike clinicians, who tend to rely on healthcare-specific tools designed around targeted clinical use cases, patients are using generalized platforms, such as Google Gemini and ChatGPT, to ask questions about their health concerns. Patients believe that these types of tools provide results that are easier to understand than medical websites and faster at offering answers than their care teams – despite concerns around data privacy and security.

Overall, this behavior indicates that basic online searches have evolved into a more active form of self-guided exploration, where individuals can quickly access, interpret, and revisit health information on their own terms.

There are certainly benefits to the patient journey becoming more self-directed, but there are also risks. Right now, we are experiencing a shared layer of AI usage that has more similarities than differences between patients and their care teams.

However, this moment of confluence may be more fragile than it seems, especially if health systems struggle to keep patient use of AI integrated into the broader clinical care ecosystem where experienced clinicians can verify AI-generated information and validate appropriate next steps.

In which areas of your role are you currently using AI?

Bar chart comparing how doctors and nurses use AI for clinical tasks, with summarizing medical data as the most common activity for both groups.

  • Graphic description

    This graphic presents two horizontal bar charts comparing how doctors and nurses report using AI across various clinical and communication tasks, with percentages indicating usage levels. Among doctors, the most common use is summarizing medical literature or analyzing data at 54%, followed by medical literature-based discovery at 49% and using AI scribes to listen and draft notes at 44%. Other uses include diagnosis support or checking drug interactions at 41%, generating patient education materials at 36%, drafting emails to patients or referral letters at 34%, and simplifying complex medical jargon into plain language for patients at 22%.

    For nurses, the most common use is also summarizing medical literature or analyzing data at 43%, followed by generating patient education materials at 41% and checking drug interactions at 37%. Additional uses include communicating with non-English speaking patients at 32%, drafting shift handoffs, discharge summaries, or nursing notes at 29%, supporting training for newer nurses at 29%, and simplifying complex medical jargon into plain language for patients at 28%.

    Overall, both groups most frequently use AI for summarizing medical information, with doctors generally reporting higher usage rates across most activities compared to nurses.

Healthcare professionals reviewing information together on a tablet in a clinical setting.
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