ConceptMamba is proposed, a conceptual-temporal framework for long-video mental health analysis that combines domain-grounded intermediate behavioral representations with efficient state-space temporal modeling and suggests that structured concept supervision is a useful direction for interpretable video-based mental health modeling.
Percival is introduced, a CT-native VLM trained on more than 400,000 CT-report pairs from the Penn Medicine BioBank using a dual-encoder symmetric contrastive framework, and evaluated against alternative foundation-model paradigms, indicating that CT-VLMs uncover latent structure aligned with clinical, physiological, a...
C. Beeche, Joonghyun Kim, H. Tavolinejad et al.· npj Digital Medicine· 1 citation
Mental health support systems are increasingly intertwined with automated systems which need clin-ically-interpreted comprehension of user input. However, the subtleties of emotionally-ladened, non-finely tuned everyday text, present limitations in comparison to generalized NLP models. This study introduces a dual-enco...
H. Para, Marapelli Bhaskar· Adolescência e Saúde· 0 citations
This study provides a scalable, objective, and explainable framework for speech-based cognitive screening, combining cognitive status classification with natural language explanations that clinicians can assess and verify, bridging the gap between advanced AI and clinical utility.
Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such models remain internally opaque. We study the interpretability of the Domain-Adapted models via Prompt-based Fine-tuning (DAPF) framework, which casts dementia detection...
Across a large-scale benchmark covering 78 datasets in 10 imaging modalities, ConceptCLIP demonstrates superior diagnostic performance while providing human-understandable explanations, and represents a critical milestone towards the widespread clinical adoption of AI.
Yuxiang Nie, Sunan He, Yequan Bie et al.· Nature Biomedical Engineerin...· 0 citations
UniAR leverages a large multimodal model to generate hierarchical diagnostic descriptions at the word, phrase, and sentence levels, compensating for the lack of paired clinical reports, and demonstrates that it offers a robust and interpretable framework for ASD screening under semantic scarcity.
Lei Xin, Ze-Heng Wang, Jia-Ying Zhu et al.· 0 citations
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