Challenges to AI use in anesthesia and healthcare: An anesthesiologist’s perspective
Bibliographic record
Abstract
Anesthesiology is well positioned to benefit from applications of artificial intelligence on multiple elements such as monitoring the depth of anesthesia, control of anesthetic machine functions, ultrasound guidance for procedures and diagnosis, adverse event prediction, pain assessment and management, and optimising the operating room workflow. The ethical concerns can arise from multiple aspects of AI research and deployment such as the nature and source of the data, data collection methodologies, AI models design, output interpretation and inappropriate use. AI solution can have the unintended consequences like perpetuation of systematic biases and discrimination towards under-represented sections of society. There could be conflicts about data protection, intellectual property rights and economic gains. Also, the research must be transparent and solutions feasible. The clinician’s role is ever changing in this landscape. We will discuss the broad ethical frameworks that are applicable to developing and using AI in medicine.
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 | 0.059 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".