The Role of Artificial Intelligence for Providing Scientific Content for Laboratory Medicine
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) promises to become an important tool in the practice of laboratory medicine. AI programs are available online that can provide concise medical and laboratory information within seconds after a question is submitted. METHODS: We posed the following contemporary and relevant question for clinical chemists using an AI program: "Should labs eliminate CK-MB testing?" The results of this inquiry were critically reviewed by a cardiologist, emergency department physician, and clinical laboratorians for accuracy and appropriateness. RESULTS: An AI report answering this question was generated within 15 s. The experts felt that the information was inaccurate with the statements that CK-MB results are released earlier than cardiac troponin (cTn), and are more useful for detection of myocardial injury in patients with renal insufficiencies. The summary omitted discussion on the ongoing debate for periprocedural detection of acute myocardial infarction (MI), the perceived value for detection of reinfarction, the value in risk stratification compared to cTn, the economic justification for testing both CK-MB and cTn, and medicolegal aspects of testing when results are discordant. CONCLUSIONS: At this time, AI does not appear to be ready to be used by clinical laboratories for answering important practice questions.
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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 | 0.076 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".