Special Issue on “Artificial Intelligence‐Driven Decision Making in Health and Medicine”
Bibliographic record
Abstract
Artificial Intelligence (AI) refers to an interdisciplinary area which embraces computer science, robotics, engineering, mathematics, and statistics, and is largely based on the ability of a machine to learn from experience, to simulate the human intelligence, to adapt to new scenarios, and to perform human-like activities.AI has revolutionized and disrupted many areas and sectors, and it is playing an ever-growing critical role in business, science, and society.It is well recognized by experts that AI will be outperforming humans on most cognitive tasks within this century, as well disrupt more than any previous technological revolution.The expression "AI-Driven Decision Making" refers to any AI technology, method, or algorithm that can support the decision-making process in a domain.AI technologies and models can compete and sometimes surpass human clinician performance in a variety of tasks and support the decision-making process in multiple medical domains.The International Transactions in Operational Research (ITOR) will publish a special issue to promote the development of AI-Driven Decision Making in Health and Medicine.Topics of interest include (but are not limited to): pattern recognition in medical images, telemedicine, natural language processing for clinical documentation, cancer screening, precision medicine, robot surgeries, virtual nursing assistant, genomic analysis, personalized treatment protocols, and operations of medical institutions.This special issue will especially focus on relevant AI applications to healthcare and medicine.It aims at shedding light on the use of AI techniques and models to search and analyse medical data, to discover insights, to uncover hidden patterns, and to help with the decision-making process, the patient-doctor interaction, the health outcome, the medical diagnosis, and the overall patient experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".