The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook
Bibliographic record
Abstract
The rise of artificial intelligence (AI)-driven tools like ChatGPT is transforming professional fields, including pathology. This study provides early insights into how pathology trainees and practicing pathologists are integrating AI into their training and clinical practice. To assess adoption, usage patterns, perceptions, and challenges related to AI-driven tools, including large language models and vision-language models, among pathology professionals. The study also explores future directions for AI integration. A cross-sectional, anonymous survey was distributed electronically to pathology residents, fellows, and attending pathologists through the Accreditation Council for Graduate Medical Education program director registry, professional organizations, and social media (X, Reddit, LinkedIn, and The Pathologist email listserv). The survey included multiple-choice, Likert-scale, and open-ended questions on AI familiarity, usage, perceived benefits/risks, and institutional policies. Data were analyzed using descriptive and inferential statistics, with qualitative responses categorized thematically. A total of 268 respondents participated, primarily residents (41%), attendings (39%), and fellows (7%), representing 23 countries (65% from the USA). Most were affiliated with academic medical centers (72%) and aged 25–44. Whereas 73% reported some familiarity with AI, actual use was limited, 31% reported rare use and 29% no use at all, especially among residents and attendings. ChatGPT was the most used tool (84%), applied mainly for document drafting (57%), research (54%), and administrative tasks (34%). Diagnostic use was minimal. Top concerns included accuracy (81%), over-reliance (65%), and data security (63%). Only 10% reported having clear institutional AI guidelines. Familiarity was strongly associated with usage frequency ( p < 0.00001). AI is increasingly used in non-diagnostic areas of pathology but adoption remains cautious. Significant gaps in clinical application, trust, and institutional support persist. Clear guidelines, targeted education, and robust validation are essential for safe, effective AI integration into pathology practice and training. • Most respondents were only somewhat familiar with AI and rarely used it, favoring ChatGPT when they did. • AI was used mostly for drafting, research, and administrative tasks, not diagnostics. • Respondents were mainly concerned about AI errors, privacy, and unclear guidelines. • Views on AI were mixed, with some seeing benefits for efficiency and others worried about reliability and job loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".