arXiv:2512.06161v1 Announce Type: cross 
Abstract: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose rising prevalence places increasing demands on a lengthy diagnostic process. Machine learning (ML) has shown promise in automating ASD diagnosis, but most existing models operate as black boxes and are typically trained on a single dataset, limiting their generalizability. In this study, we introduce a transparent and interpretable ML approach that leverages BioBERT, a state-of-the-art language model, to analyze unstructured clinical text. The model is trained to label descriptions of behaviors and map them to diagnostic criteria, which are then used to assign a final label (ASD or not). We evaluate transfer learning, the ability to transfer knowledge to new data, using two distinct real-world datasets. We trained on datasets sequentially and mixed together and compared the performance of the best models and their ability to transfer to new data. We also created a black-box approach and repeated this transfer process for comparison. Our transparent model demonstrated robust performance, with the mixed-data training strategy yielding the best results (97 % sensitivity, 98 % specificity). Sequential training across datasets led to a slight drop in performance, highlighting the importance of training data order. The black-box model performed worse (90 % sensitivity, 96 % specificity) when trained sequentially or with mixed data. Overall, our transparent approach outperformed the black-box approach. Mixing datasets during training resulted in slightly better performance and should be the preferred approach when practically possible. This work paves the way for more trustworthy, generalizable, and clinically actionable AI tools in neurodevelopmental diagnostics.

قدمت دراسة حديثة نهجًا جديدًا للتعلم الآلي للكشف عن اضطراب طيف التوحد (ASD) من خلال تحليل الملاحظات السريرية غير المنظمة باستخدام BioBERT، وهو نموذج لغوي متطور. تهدف هذه الطريقة إلى تحسين عملية التشخيص، التي غالبًا ما تكون طويلة ومعقدة، من خلال توفير نموذج شفاف وقابل للتفسير يقوم بتسمية أوصاف السلوك وفقًا للمعايير التشخيصية.

Un estudio reciente ha introducido un nuevo enfoque de aprendizaje automático para detectar el trastorno del espectro autista (TEA) mediante el análisis de notas clínicas no estructuradas utilizando BioBERT, un modelo de lenguaje de última generación. Este método busca mejorar el proceso de diagnóstico, que a menudo es largo y complejo, al proporcionar un modelo transparente e interpretable que etiqueta las descripciones de comportamientos según criterios diagnósticos.

Une étude récente a introduit une nouvelle approche d'apprentissage automatique pour détecter le trouble du spectre autistique (TSA) en analysant des notes cliniques non structurées à l'aide de BioBERT, un modèle de langage à la pointe de la technologie. Cette méthode vise à améliorer le processus de diagnostic, souvent long et complexe, en fournissant un modèle transparent et interprétable qui étiquette les descriptions comportementales selon des critères diagnostiques.

A recent study has introduced a novel machine learning approach for detecting autism spectrum disorder (ASD) by analyzing unstructured clinical notes using BioBERT, a state-of-the-art language model. This method aims to improve the diagnostic process, which is often lengthy and complex, by providing a transparent and interpretable model that labels behavioral descriptions according to diagnostic criteria.

Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach

arXiv:2505.07917v2 Announce Type: replace-cross 
Abstract: Biomedical question-answering (QA) systems require effective retrieval and generation components to ensure accuracy, efficiency, and scalability. This study systematically examines a Retrieval-Augmented Generation (RAG) system for biomedical QA, evaluating retrieval strategies and response time trade-offs. We first assess state-of-the-art retrieval methods, including BM25, BioBERT, MedCPT, and a hybrid approach, alongside common data stores such as Elasticsearch, MongoDB, and FAISS, on a ~10% subset of PubMed (2.4M documents) to measure indexing efficiency, retrieval latency, and retriever performance in the end-to-end RAG system. Based on these insights, we deploy the final RAG system on the full 24M PubMed corpus, comparing different retrievers' impact on overall performance. Evaluations of the retrieval depth show that retrieving 50 documents with BM25 before reranking with MedCPT optimally balances accuracy (0.90), recall (0.90), and response time (1.91s). BM25 retrieval time remains stable (82ms), while MedCPT incurs the main computational cost. These results highlight previously not well-known trade-offs in retrieval depth, efficiency, and scalability for biomedical QA. With open-source code, the system is fully reproducible and extensible.

قدمت دراسة نظامًا لتوليد الأسئلة البيولوجية المعززة بالاسترجاع (RAG) ، حيث تم تقييم استراتيجيات الاسترجاع المختلفة وكفاءتها على مجموعة فرعية من بيانات PubMed. تقيم الدراسة طرقًا متطورة مثل BM25 و BioBERT ، وتقيس كفاءة الفهرسة وأداء الاسترجاع قبل نشر النظام على مجموعة بيانات PubMed الكاملة.

Un estudio ha presentado un sistema de Generación Aumentada por Recuperación (RAG) para la respuesta a preguntas biomédicas, evaluando diversas estrategias de recuperación y su eficiencia en un subconjunto de datos de PubMed. La investigación evalúa métodos de vanguardia como BM25 y BioBERT, midiendo la eficiencia de indexación y el rendimiento de recuperación antes de implementar el sistema en el corpus completo de PubMed.

Une étude a introduit un système de génération augmentée par récupération (RAG) pour le questionnement biomédical, évaluant diverses stratégies de récupération et leur efficacité sur un sous-ensemble de données de PubMed. La recherche évalue des méthodes à la pointe comme BM25 et BioBERT, mesurant l'efficacité de l'indexation et la performance de récupération avant de déployer le système sur l'ensemble du corpus de PubMed.

A study has introduced a Retrieval-Augmented Generation (RAG) system for biomedical question answering, evaluating various retrieval strategies and their efficiency on a subset of PubMed data. The research assesses state-of-the-art methods like BM25 and BioBERT, measuring indexing efficiency and retrieval performance before deploying the system on the full PubMed corpus.

Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation

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Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach

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