Possible Implications of Artificial Intelligence on Obstetrics and Gynecology and Medicine in the Next Few Decades
Bibliographic record
Abstract
Artificial intelligence will change work for most people in significant and unexpected fashions in the next few decades. A change similar to that seen with the industrial revolution of the 19th century. Certain jobs will cease to exist while new employment will be created. The implication of this transformation in medicine and obstetrics and gynecology in particular needs discussion, as it stands it is anxiety-provoking. Artificial intelligence will have implications on the number of physicians needed in certain specialties, the workloads of those physicians, and the ease of accessing information. In the field of reproductive endocrinology, artificial intelligence is already being used to select embryos with the greatest potential for implantation. Who will develop that technology and the drivers for development will also be considered. Physicians, insurance companies, and other funders of health care need to be informed to anticipate and prepare for these changes. As such we will discuss anticipated changes in the near future to be initiated by artificial intelligence, we anticipate physician quality of life will improve while the demonstrated anxiety is unfounded.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".