Papers with doctors
Leveraging Language Models for Summarizing Mental State Examinations: A Comprehensive Evaluation and Dataset Release (2025.coling-main)
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| Challenge: | Mental health disorders affect a significant portion of the global population . access to mental health support is limited in developing countries . |
| Approach: | They evaluated a 12-item descriptive MSE questionnaire and five well-known summarization models . they found that language models can generate coherent MSE summaries for doctors . |
| Outcome: | The proposed model can generate coherent summaries from MSEs in a conversational format. |
Predicting in-hospital mortality by combining clinical notes with time-series data (2021.findings-acl)
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| Challenge: | In intensive care units, patient health is monitored through vital signals and clinical notes . previous work focused on predicting patient health using time-series data gathered from medical devices . |
| Approach: | They propose a model that combines clinical notes and vital data to make accurate mortality predictions. |
| Outcome: | The proposed model achieves an AUC score of 0.9, compared to the previous 0.87 . it can be used to make accurate in-hospital mortality predictions . |
Polish-English medical knowledge transfer: A new benchmark and results (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated significant potential in specialized tasks, including medical problem-solving. |
| Approach: | They propose to use a Polish medical licensing and specialization exam dataset to evaluate LLMs . they use exam questions and parallel Polish-English corpora professionally translated for foreign candidates . |
| Outcome: | The proposed dataset includes Polish exam questions and parallel Polish-English corpora professionally translated for foreign candidates. |
HuatuoGPT, Towards Taming Language Model to Be a Doctor (2023.findings-emnlp)
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Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li
| Challenge: | Experimental results show that the distilled language model outperforms its teacher model (ChatGPT) in most cases. |
| Approach: | They propose a Large Language Model (LLM) that leverages both distilled data from **ChatGPT** and real-world data from**doctors** in the supervised fine-tuning stage. |
| Outcome: | The proposed model outperforms the teacher model in most cases by using additional real-world data and RLMF to align the language model with the merits of both sources. |
MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations (2024.emnlp-main)
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| Challenge: | Existing datasets lacking comprehensive annotations for medical history-taking are non-English . existing datasets lack comprehensive annotation for medical slots and their attributes . |
| Approach: | They propose a dataset of doctor-patient dialogues in English for medical history-taking task. |
| Outcome: | The proposed datasets are available in English and are compared with existing datasets. |
Follow-up Question Generation For Enhanced Patient-Provider Conversations (2025.acl-long)
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Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Inas S. Khayal, Sarah DeLozier, Sarah Masud Preum
| Challenge: | Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. |
| Approach: | They propose a framework that generates personalized follow-up questions based on patient utterances and prior EHR data. |
| Outcome: | The framework reduces follow-up communications by 34% and improves performance by 17% and 5% on real and synthetic data. |