A Comprehensive Framework for the Development of Ethical Machine Learning in Medicine
Bibliographic record
Abstract
As the health industry continues to collect more data and the development of Artificial Intelligence continues to reach new heights, the potential collaboration between the two becomes more tempting.Leveraging the power of AI and the sheer amount of data within health could revolutionize the health industry that is known today.However, it is imperative that the ethics of such innovative solutions are considered as while the potential for AI in medicine is astronomical, the potential pitfalls are treacherous.In order to ensure the ethical use of Machine Learning and AI in medicine, a recognized set of ethical guidelines must be put into place for the development of models.AI experts and health professionals alike must work together to consider the ethical quandaries of the usage of AI in medicine and develop methods and guidelines to mitigate them.We have surveyed the current usage and ethical concerns of AI in medicine and the state of ethical machine learning in Computer Science.Through this survey it has become clear that there is a need for a comprehensive framework for ethical machine learning development with applications in medicine.While there is work being done for each stage of the development of machine learning in medicine in an attempt to create ethical models, there are none that cover all of the stages nor are they covering more than a few ethical issues.There is a need for an interdisciplinary, comprehensive framework combining the best of AI Impact Assessments (AIAs), quantitative and qualitative metrics, checklists, and the numerous debates and discussions on the ethics of AI in medicine.
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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 | 0.186 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.012 | 0.079 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".