Roundtable Discussion III: The Development and Uses of Artificial Intelligence in Medicine: A Work in Progress
Bibliographic record
Abstract
Humans have devised machines to replace computation by individuals since ancient times: The abacus predated the written Hindu–Arabic numeral system by centuries. We owe a quantum leap in the development of machines to help problem solve to the British mathematician Charles Babbage who built what he called the Difference Engine in the mid-19th century. But the Turing formula created in 1936 is the foundation for the modern computer; it produced printed symbols on paper tape that listed a series of logical instructions. Three decades later, Olivetti manufactured the first mass-marketed desktop computer (1964), and by 1981, IBM had developed the first personal computer. Computing machines have become more and more powerful, culminating recently in Google’s claim that it had achieved quantum supremacy in developing a system that can complete a task in 200 seconds that it would take the most powerful type of classical computer available 10 000 years to achieve. In short, we are in a period of human history in which we are creating more and more powerful and complex machines potentially capable of duplicating human intelligence and indeed surpassing/expanding its power. We are solidly in the age of artificial intelligence (AI). Increasing interest in the development of AI and its application to human health at all levels makes a roundtable discussion by experts a valuable project for publication in our journal, Gender and the Genome, the official journal of the Foundation for Gender-Specific Medicine and the International Society of Gender 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 | 0.039 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.073 | 0.065 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".