A note on certain implications of clinical artificial intelligences for the field of medico-legal semiotics
Bibliographic record
Abstract
Artificial intelligence has profound implications for the filed of clinical practices, and also for semiotics and law. In this article, we articulate and explain the different types of Clinical artificial intelligence (CAIs) as their normativity often stems from their type (symbolic or connectionist) (Harnad, 1990), and relative autonomy/agency. Older, symbolic AI, while more explainable, did not offer the potential that offer the current, second generation CAIs. The intelligibility of the reasoning used by CAIs remains largely opaque and generally unintelligible and unexplainable for human interpreters, even sometimes counter-factual (Lee & Topol, 2024). This is also true of the most recent so-called “explainable” AIs, that remains imperfect and only very partially explainable (Reddy, 2022). The most recent literature reveals that the very question of AI explainability continues to be one of the most heavily debated concerning CAIs (Hildt, 2025). In this article, we will reveal that the solution to the black-box problem of CAIs resides in an investigation in the (bio)semiotic nature of both CAIs themselves, but also the problem that surround their explainability. We conclude with solutions to promote transparency in the use of CAIs.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".