Artificial Intelligence‐Based Virtual Assistant for the Diagnostic Approach of Chronic Ataxias
Bibliographic record
Abstract
BACKGROUND: Chronic ataxias, a complex group of over 300 diseases, pose significant diagnostic challenges because of their clinical and genetic heterogeneity. Here, we propose that artificial intelligence (AI) can aid in the identification and understanding of these disorders through the utilization of a smart virtual assistant. OBJECTIVES: The aim is to develop and validate an AI-powered virtual assistant for diagnosing chronic ataxias. METHODS: A non-commercial virtual assistant was developed using advanced algorithms, decision trees, and large language models. In the validation process, 453 clinical cases from the literature were selected from 151 causes of chronic ataxia. The diagnostic accuracy was compared with that of 21 neurologists specializing in movement disorders and GPT-4. Usability regarding time and number of questions needed were also evaluated. RESULTS: The virtual assistant accuracy was 90.9%, higher than neurologists (18.3%), and GPT-4 (19.4%). It also significantly outperformed in causes of ataxia distributed by age, inheritance, frequency, associated clinical manifestations, and treatment availability. Neurologists and GPT-4 mentioned 110 incorrect diagnoses, 83.6% of which were made by GPT-4, which also generated seven data hallucinations. The virtual assistant required an average of 14 questions and 1.5 minutes to generate a list of differential diagnoses, significantly faster than the neurologists (mean, 19.4 minutes). CONCLUSIONS: The virtual assistant proved to be accurate and easy fast-use for the diagnosis of chronic ataxias, potentially serving as a support tool in neurological consultation. This diagnostic approach could also be expanded to other neurological and non-neurological diseases. © 2025 International Parkinson and Movement Disorder Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".