The future of artificial intelligence in medicine: Medical-legal considerations for health leaders
Bibliographic record
Abstract
Artificial Intelligence (AI) is becoming increasingly common in healthcare and has potential to improve the efficiency and quality of healthcare services. As the utility of AI expands, medical-legal questions arise regarding the possible legal implications of incorporating AI into clinical practice. Particularly, the unique black box nature of AI brings distinct challenges. There is limited guidance addressing liability when AI is used in clinical practice, and traditional legal principles present limitations when applied to novel uses of AI. Comprehensive solutions to address the challenges of AI have not been well established in North America. As AI continues to evolve in healthcare, appropriate guidance from professional regulatory bodies may help the medical field realize AI's utility and encourage its safe use. As the options for AI in medicine evolve, physicians and health leaders would be prudent to consider the evolving medical-legal context regarding use of AI in clinical practices and facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.033 | 0.036 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".