Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It
Bibliographic record
Abstract
With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.
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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 | 0.041 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.030 | 0.041 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".