AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations
Bibliographic record
Abstract
Unlabelled: Recent applications of artificial intelligence (AI) and machine learning in medicine, psychology, and social sciences have led to common terminological confusions. In this paper, we review emerging evidence from systematic reviews documenting widespread misuse of key terms, particularly "prediction" being applied to studies merely demonstrating association or retrospective analysis. We clarify when "prediction" should be used and recommend using "prospective prediction" for future prediction; explain validation procedures essential for model generalizability; discuss overfitting and generalization in machine learning and traditional regression methods; clarify relationships between features, independent variables, predictors, risk factors, and causal factors; and clarify the hierarchical relationship between AI, machine learning, deep learning, large language models, and generative AI. We provide evidence-based recommendations for terminology use that can facilitate clearer communication among researchers from different disciplines and between the research community and the public, ultimately advancing the rigorous application of AI in medicine, psychology, and social sciences.
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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 | 0.046 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".