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Medical education: How are residents using AI? (opens in a new tab)

medicalxpress.com · 2026-10-26

Short answerEvidenceSource

Short answer

Mostly not supported

Mostly not supported.

One key claim is not backed by the study. One other point was not covered by the paper.

  • 1 supported
  • 1 overstated
  • 1 not supported
  • 1 not covered

Checked against the study summary. The full text wasn't available, so some details couldn't be settled either way.

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Follow the evidence trail
1
2

NewsLink checks it

Mostly not supported

Two claims go beyond the study. One overstates it and one isn't supported at all. One claim the study doesn't address.

  • 1 supported
  • 1 overstated
  • 1 not supported
  • 1 not covered
Open claim evidence
3
Then inspect each claim

Evidence layer

Claim by claim

Each claim gets a verdict. Expand it to see the evidence directly below.

4 claims in this story

Showing all 4 claimsChoose a verdict to focus the list.

Claim 1 of 4Not supportedOf the residents who reported using AI, only 36% said they had received training in the last six months.View evidenceHide evidence

As stated36%

Why this verdict

The profile reports that 36% of residents/respondents reported any AI education or training in the prior six months. The story instead frames 36% as applying to the subset of residents who used AI, which is a different denominator and is not supported by the abstract evidence.

1

Study evidence

Thirty-six percent of family medicine residents reported any education or training about AI use in the prior 6 months.36%

“Residents were asked about ... any education about AI use they received in the last 6 months.”
Claim 2 of 4OverstatedBarr says residents need training in how to use AI tools correctly, and the solution is to incorporate AI training with critical appraisal into medical school and residency curricula.View evidenceHide evidence

Why this verdict

The paper profile supports the authors' inference that formal AI training appears limited relative to adoption and that new appraisal skills and teaching/assessment methods are needed. But the story states more definitively that the solution is to incorporate AI training into medical school and residency curricula, and attributes this specifically to Barr; those stronger and more specific claims are not established in the abstract-level profile.

1

Study evidence

High prevalence of recent AI use among FM residents: 10,858 of 15,259 respondents (71.1%) reported using AI in the last month.71.1% (10,858/15,259)

“The 2025 American Board of Family Medicine (ABFM) National Resident Survey (NRS) was administered in October 2025 to all FM residents who completed the In-Training Exam.”
2

Study evidence

Thirty-six percent of family medicine residents reported any education or training about AI use in the prior 6 months.36%

“Residents were asked about ... any education about AI use they received in the last 6 months.”
Claim 3 of 4Not coveredA new study led by Yale School of Medicine's Wendy Barr evaluated AI use among more than 15,000 family medicine residency trainees across the U.S.View evidenceHide evidence

As statedmore than 15,000

Why this verdict

The abstract-level profile supports that the study was a national cross-sectional survey of family medicine residents with 15,259 respondents completing AI questions. However, the specific attribution that it was led by Yale School of Medicine's Wendy Barr is not available in the supplied abstract profile, so that part is not verifiable at this depth.

1

Study evidence

High prevalence of recent AI use among FM residents: 10,858 of 15,259 respondents (71.1%) reported using AI in the last month.71.1% (10,858/15,259)

“The 2025 American Board of Family Medicine (ABFM) National Resident Survey (NRS) was administered in October 2025 to all FM residents who completed the In-Training Exam.”
Claim 4 of 4SupportedIn October 2025, 71% of residents had used AI for work in the past month.View evidenceHide evidence

As stated71%

Why this verdict

The paper profile states that the 2025 ABFM National Resident Survey was administered in October 2025 and that 10,858 of 15,259 residents, or 71.1%, reported AI use in the last month. The story's 71% figure is an accurate rounding.

1

Study evidence

High prevalence of recent AI use among FM residents: 10,858 of 15,259 respondents (71.1%) reported using AI in the last month.71.1% (10,858/15,259)

“The 2025 American Board of Family Medicine (ABFM) National Resident Survey (NRS) was administered in October 2025 to all FM residents who completed the In-Training Exam.”
Then look for missing context

Context layer

What the story left out

Important study details the story did not include.

  • The paper also reports patterns of AI use, including medical reference, clinical decision support, learning outside patient care, and ambient documentation.

    The story focuses on overall AI use and training but does not describe the paper's reported use categories or their percentages.

    From Cross-sectional national survey (ABFM National Resident Survey)

  • Training finding: 36% of all respondents reported any AI education or training in the prior six months.

    The story includes the 36% figure but misstates the denominator as AI users rather than all respondents/residents, so the paper element is not accurately reflected.

    From Cross-sectional national survey (ABFM National Resident Survey)

  • Key limitation: AI use and AI training exposure were self-reported and subject to reporting or recall bias.

    The story's caveats mention uncertainty about AI effects and future research, but they do not acknowledge that the main measures were self-reported.

    From Cross-sectional national survey (ABFM National Resident Survey); Cross-sectional national survey (ABFM National Resident

  • Key limitation: the cross-sectional descriptive design limits inference about change over time, causality, or effects on clinical decision-making and outcomes.

    The story notes that researchers are still learning how AI affects clinical decision-making, but it does not clearly state that this survey is cross-sectional and does not measure causal effects or outcomes.

    From Cross-sectional national survey (ABFM National Resident Survey); Cross-sectional national survey (ABFM National Resident

  • Key limitation: the abstract does not characterize frequency, intensity, specific AI tools used, or the quality/content of AI training.

    The story does not mention these measurement limitations, which matter for interpreting what 'AI use' and 'training' mean.

    From Cross-sectional national survey (ABFM National Resident Survey); Cross-sectional national survey (ABFM National Resident

  • Supporting method element: resident demographics were obtained from ABFM administrative databases, but the abstract provides no demographic breakdowns.

    The story does not discuss the administrative demographic data source or the absence of demographic detail in the abstract.

    From secondary_data (administrative database linkage)

4 things the story did carry across
  • The study was a national, cross-sectional ABFM National Resident Survey administered in October 2025 to family medicine residents completing the In-Training Exam.
  • Large respondent count: 15,259 residents completed the AI questions, with a reported 99.6% response rate.
  • Primary finding: 71.1% of respondents reported using AI in the prior month.
  • Authors interpret the results as a gap between AI adoption and structured competency development, implying a need for AI-related appraisal skills and teaching/assessment methods.
Then read the study layer

Study layer

Study at a glance

Scan the study first. Expand only the parts you want to inspect.

Pieces of work

3

Evidence read

study summary

Lead result

secondary data

1Lead resultsecondary dataQuantify prevalence and patterns of recent (last-month) AI tool use among US family medicine residents.Cross-sectional national survey (ABFM National Resident Survey)Expand

In plain English

Cross-sectional, census-style national survey (2025 ABFM National Resident Survey) of family medicine residents who completed the In-Training Exam estimating recent (last-month) self-reported AI tool use and patterns of use; 71.1% reported any AI use in the prior month, with medical reference and clinical decision support the most common use-categories.

Key findings

  • High prevalence of recent AI use among FM residents: 10,858 of 15,259 respondents (71.1%) reported using AI in the last month.71.1% (10,858/15,259)
  • Patterns of reported AI use: the most common reported purposes were medical reference, clinical decision support, learning outside patient care, and ambient documentation.Medical reference 40.7%; Clinical decision support 31.9%; Learning outside patient care 23.6%; Ambient documentation 21.9%
“The 2025 American Board of Family Medicine (ABFM) National Resident Survey (NRS) was administered in October 2025 to all FM residents who completed the In-Training Exam.”
What this piece can’t prove
  • Measures are self-reported and subject to reporting bias (abstract-reported data).
  • Cross-sectional design limits inference about changes over time or causality.
  • Abstract provides category-level use in the prior month but not frequency, intensity, or specific AI tools used.

1 further detail could not be confirmed from the summary.

2secondary dataAssess prevalence of recent (last-6-month) education/training about AI use among family medicine residents, and characterize the gap between AI adoption and formal training.Cross-sectional national survey (ABFM National Resident Survey)Expand

In plain English

In the 2025 ABFM National Resident Survey of family medicine residents, 36% of respondents reported receiving any education or training about AI use in the prior 6 months, indicating limited formal training relative to reported AI adoption.

Key findings

  • Thirty-six percent of family medicine residents reported any education or training about AI use in the prior 6 months.36%
“Residents were asked about ... any education about AI use they received in the last 6 months.”
What this piece can’t prove
  • Self-reported single-item measure of AI education/training; details on content, duration, or quality of training not provided.
  • Cross-sectional design and 6-month recall window limit temporal resolution and causal inference.
  • Abstract does not provide the survey question wording or breakdowns by resident characteristics that might affect training prevalence.
3secondary dataDescribe resident demographics (from ABFM administrative databases) in relation to AI use/training responses (descriptive context for interpretation).secondary data (administrative database linkage)Expand

In plain English

The study linked survey responses to administrative resident demographic data from the American Board of Family Medicine (ABFM) databases to describe the respondent sample/frame; the abstract states that demographics were obtained from ABFM administrative databases but provides no demographic breakdowns in the abstract.

Key findings

  • Resident demographic data were sourced from ABFM administrative databases and used to describe the survey respondents/sample frame.
“Resident demographics were obtained from ABFM administrative databases.”
What this piece can’t prove

3 further details could not be confirmed from the summary.

Finally, the search trail

Method layer

NewsLink found the paper. Tessa takes you deeper.

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Open the paper in Tessa

Artificial Intelligence Use and Training in Residency: Results of a National Family Medicine Survey

Family Medicine · 2026

Source record (opens in a new tab)

Why this one

Near certain

NewsLink found the paper. Tessa is where you inspect it deeply.

Papers considered

The selected paper, plus nearby candidates.

Crossref, PubMed, Europe PMC · 16 candidate papers

Selected

Artificial Intelligence Use and Training in Residency: Results of a National Family Medicine Survey

Family Medicine · 2026 · Crossref

Source (opens in a new tab)
Candidate

Survey of Secure Network Protocols: United States Related Domains

Artificial Intelligence and Machine Learning · 2022 · Crossref

Source (opens in a new tab)

And 10 more candidates considered.