The study examined how language‑model raters assess depression using the Patient Health Questionnaire across 880 raters and 189 interviews. Model choice accounted for 30% of symptom‑score variance, while stable participant differences explained 10.5%. Even raters with similar overall accuracy (AUC ≥ 0.70) disagreed on screening decisions for 40% of participants, and only recalibration with labeled data improved agreement and accuracy modestly.
arXiv:2606. 03198v1 Announce Type: cross Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluation conditions has not been quantitatively characterized.
The study introduces MedQADE, a German open‑response clinical benchmark with 3,800 question‑answer pairs and physician reference annotations. It evaluates large language models (LLMs) as judges, finding that while some LLMs (e.g., Gemini 3 Flash) achieve physician‑level agreement on correctness, they exhibit self‑bias and low abstention rates. Physicians showed moderate agreement on correctness but limited agreement on difficulty, and their abstention increased with perceived difficulty.
arXiv:2608. 08746v1 Announce Type: new Abstract: Prospective daily symptom tracking is central to premenstrual health assessment, but repeated ordinal forms impose substantial response burden.
arXiv:2609.15855v1 Announce Type: cross Abstract: % !TEX root = ../main.tex People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk con...
The study examines how large language models (LLMs) predict depression scores from language responses. In a "Mirror" setup, participants answered structured diagnostic interviews that the LLMs used to predict scores, yielding near-perfect predictions. In a "Non-Mirror" setup, participants gave life history interviews; the LLMs still achieved outstanding prediction accuracy, and both conditions correlated similarly with PHQ-9 scores, indicating that the Mirror advantage disappears when predicting an independent measure. Topic modeling showed different depression themes across interview types, suggesting Mirror evaluations are more about reliability than validity and that Non-Mirror approaches may enhance clinical relevance.
arXiv:2607. 18828v1 Announce Type: new Abstract: Readiness stress-testing of medical AI has focused on closed-ended and multimodal benchmarks.
The study investigates how a large language model, Gemma-3-27B-PT, internally represents depressive symptoms. By applying mechanistic interpretability methods to the model’s residual stream, researchers found that symptom groups are geometrically distinct at layer 21, and that projected symptom vectors align with clinician-annotated rankings across mood, somatic, and suicidality dimensions. Additionally, a single depression vector at this layer can differentiate depressive from non-depressive text with an AUC of 0.789, suggesting a potential emotional valence gate for symptom projection.
The study investigates how large language models (LLMs) assess psychological distress in online posts from six identity‑based communities. Through a perspectivist annotation task, 321 participants provided 9,587 judgments on 1,198 Reddit posts, revealing modest in‑group agreement (OR = 1.18) that varies across communities. When evaluated against these community‑specific labels, open‑weight LLMs consistently over‑estimate distress—achieving only 31–44% accuracy on posts perceived as none‑to‑mild—while newer models like GPT‑5 and Gemini 2.5 Pro show similar inflation, whereas Claude Opus 4 is more conservative. "whyItMatters":"The findings highlight that miscalibrated distress detection by LLMs can disproportionately impact the very communities they aim to serve, underscoring the need for equitable AI deployment in mental‑health contexts."
arXiv:2608.31007v1 Announce Type: cross Abstract: Understanding how psychiatric patients subjectively experienced a clinical conversation is important for feedback and alliance-related process monito...
The paper examines automatic depression detection from doctor‑patient conversations and finds that models trained on semi‑structured interview data can achieve high accuracy by exploiting fixed interviewer prompts rather than the participants’ language. Across three datasets (ANDROIDS, DAIC‑WOZ, E‑DAIC), the authors show that restricting models to participant utterances distributes decision evidence more broadly and reflects genuine linguistic cues. The study highlights a cross‑dataset, architecture‑agnostic bias introduced by interviewer prompts and calls for analyses that localize decision evidence by time and speaker to ensure models learn from participants’ language.
arXiv:2609.39049v1 Announce Type: cross Abstract: A large language model (LLM) can rate depression severity directly from a social media post or mark which clinical criteria the post shows and let co...
arXiv:2608. 14552v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty.