Abstract IA-15: Platforms to improve reproducibility in artificial intelligence research
Bibliographic record
Abstract
Abstract As artificial intelligence (AI) becomes a method of choice to analyze biomedical data, the field is facing multiple challenges around research reproducibility and transparency. Given the proliferation of studies investigating the applications of AI in research and clinical studies, it is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies. Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of AI algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related AI research. In this talk, I will describe the various aspects of an AI biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community. Citation Format: Benjamin Haibe-Kains, Anthony Mammoliti, Minoru Nakano. Platforms to improve reproducibility in artificial intelligence research [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-15.
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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 | 0.115 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".