Artificial Intelligence Software Changes Rare Disease Testing Strategy in Real Time: An International Case Series Using <scp>Face2Gene</scp>
Bibliographic record
Abstract
Genetic disorders commonly share features such as developmental delays, cognitive impairment, and behavioral challenges, yet many conditions also present unique dysmorphic features that distinguish them. Performing a thorough medical and family history and a detailed physical exam with attention to dysmorphic features is often the first step toward arriving at a genetic diagnosis. Synthesizing a differential diagnosis from the information gathered helps to guide the genetic testing strategy. Challenges to recognizing a disorder include the breadth of the clinician's prior experience, the lack of distinctive dysmorphology or overlapping dysmorphology in some conditions, atypical presentations, and difficulties identifying phenotypes across different ancestries. In cases where such challenges exist, advanced facial recognition technology can help the consulting expert by directing a more efficient test strategy. We present 17 cases involving 19 patients, including one pair of affected siblings and one case involving a child and her affected mother, for which DeepGestalt, the technology powering Face2Gene, changed medical geneticists' testing decisions. These cases illustrate how this form of artificial intelligence can provide clinical utility through influencing providers' genetic testing recommendations in real time.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".