Impact of Artificial Intelligence, With and Without Information, on Pathologists’ Decisions: An Experiment
Bibliographic record
Abstract
Abstract Background: Artificial intelligence (AI) is rapidly gaining attention in medicine and in pathology in particular. While much progress has been made in refining the accuracy of algorithms, thereby increasing their potential use, we need to better understand how these algorithms will be used by pathologists, who will remain for the foreseeable future the decision-makers. The objective of this paper is to determine the propensity of pathologists to rely on AI decision aids and to investigate whether providing information on the algorithm impacts this reliance.Methods: To test our hypotheses, we conducted an experiment with within-subjects design using an online survey study. 116 respondent pathologists and pathology students participated in the experiment. Each participant was tasked with assessing the Gleason grade for a series of 12 prostate cancer samples under three conditions: without advice, with advice from an AI decision aid, and with advice from an AI decision aid with information provided on the algorithm, namely the algorithm accuracy rate and the algorithm model. Scores were computed by comparing the respondents’ scores with the “true” score at the individual-question level. A mixed effects logistic regression was used to analyze the difference in scores between the different conditions, controlling for the random effects of participants and images and to assess the interactions with Experience, Gender and beliefs towards AI.Results: Participant responses to the questions with AI decision aids were significantly more accurate than the control condition without aid. However, no significant difference was found when subjects were provided with additional accuracy rate and model information on the AI advice. Moreover, the propensity to rely on AI was found to relate to general beliefs on AI but not with particular assessments of the AI tool offered. Males also performed better in the No-aid condition but not in the AI-aid condition.Conclusions: AI can significantly influence pathologists and the general beliefs in AI could be major predictors of future reliance on AI by pathologists.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".