Papers for

healthcare chatbot developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Medical fact verification improves with snippet level clinical grouping

MedSNIP: Building and Benchmarking Snippet-Level Granularity for Medical Fact Verification

Abstract: A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range, a causal or conditional link, or patient-specific details to be judged correctly, and atom-level decomposition can fragment these dependencies, leaving the verifier with clinically incomplete claims. We reformulate medical fact-checking around snippet-level verification, where clause-grouped units preserve local clinical structure. We introduce MedSNIP-Bench, a human-annotated benchmark for snippet-level medical fact verification, and MedSNIP, an automatic snippet-generation pipeline. MedSNIP-Bench covers 276 consumer-health and clinical-vignette responses, segmented into 2,524 snippets with dual in-general and in-patient-context labels and six structural pattern codes. MedSNIP is evaluated against human snippet boundaries on MedSNIP-Bench and then used to generate snippet-level units for external corpora. Across MedSNIP-Bench, HealthFC, and MedHallu, snippet-level verification preserves or improves false-class F1, with gains concentrated where answers are long enough to fragment and where the verifier is strong enough to exploit the recovered structure. The largest merge-pattern gain is on causal-conditional clinical chains. It also reduces verifier calls by 24-73%, though the saving survives end-to-end only when decomposition is cheap, which an open-weight decomposer makes possible at no loss of chunking fidelity.

Fri 11 SeptComputation and Language

The gist

Medical claims often depend on detailed clinical context, like lab results or patient-specific factors, to be judged correctly. The authors propose checking claims by splitting them into meaningful snippet units that keep clinical details together, instead of breaking them into too small pieces. They created a new dataset and a method for automatically generating these snippets, which helps improve the accuracy of verifying medical facts. This approach reduces unnecessary checks and works especially well for complex medical claims with causal or conditional relationships.

Open → 2609.12884v1

EmoMed adapts medical advice using emotions and updated facts

EmoMed: An Emotionally-Aware Agent for Multimodal Medical Support with Real-Time Information Retrieval

Abstract: We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users' emotional states while maintaining clinical accuracy. The system processes text and medical images, detects affect indicators (anxiety, confusion, urgency) from user input, and adjusts response tone, structure, and detail level accordingly. To ensure factual reliability, the agent grounds clinical information through a dual retrieval mechanism: web-based fact-checking and an API-connected, continuously updated medical knowledge base. We evaluate our approach across seven state-of-the-art language models (GPT-4/5, Qwen3, Llama 4, Gemini 2.5, Grok4, Claude3) using comprehensive metrics including LLM-as-judge assessments, MedQA style accuracy tests, BERT Score, safety/helpfulness ratings, and multimodal medical benchmarks. The results demonstrate that emotionally adaptive responses consistently outperform neutral baseline across evaluation dimensions, without compromising clinical accuracy. A controlled user study validated these findings, with participants reporting improved perceived empathy and communication clarity, while maintaining trust in factual accuracy. Source code: https://github.com/NasonovIvan/EmoMed-Agent

Mon 7 SeptArtificial IntelligenceComputers and SocietyHuman-Computer Interaction

The gist

Medical advice can be hard to understand and sometimes not sensitive enough to how a person feels. The authors created EmoMed, a smart helper that reads your words and medical images, notices if you seem worried or confused, and changes how it talks to you to feel more caring and clear. It double-checks facts from the web and a medical database to give accurate answers. Tests show EmoMed works better than usual helpers without losing accuracy, and people say its answers feel more empathetic and easier to understand.

Open → 2609.07194v1