Objectives: Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation. Methods: KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a “chosen” response and a “rejected” response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning. Results: Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting. Conclusions KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.

Objectives: Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation. Methods: KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a “chosen” response and a “rejected” response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning. Results: Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting. Conclusions KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.

Objectives: Developing large language models (LLMs) in biomedicine requires access to high-quality training and alignment tuning datasets. However, publicly available Korean medical preference datasets are scarce, hindering the advancement of Korean medical LLMs. This study constructs and evaluates the efficacy of the Korean Medical Preference Dataset (KoMeP), an alignment tuning dataset constructed with an automated pipeline, minimizing the high costs of human annotation. Methods: KoMeP was generated using the DAHL score, an automated hallucination evaluation metric. Five LLMs (Dolly-v2-3B, MPT-7B, GPT-4o, Qwen-2-7B, Llama-3-8B) produced responses to 8,573 biomedical examination questions, from which 5,551 preference pairs were extracted. Each pair consisted of a “chosen” response and a “rejected” response, as determined by their DAHL scores. The dataset was evaluated when trained through two different alignment tuning methods, direct preference optimization (DPO) and odds ratio preference optimization (ORPO) respectively across five different models. The KorMedMCQA benchmark was employed to assess the effectiveness of alignment tuning. Results: Models trained with DPO consistently improved KorMedMCQA performance; notably, Llama-3.1-8B showed a 43.96% increase. In contrast, ORPO training produced inconsistent results. Additionally, English-to-Korean transfer learning proved effective, particularly for English-centric models like Gemma-2, whereas Korean-to-English transfer learning achieved limited success. Instruction tuning with KoMeP yielded mixed outcomes, which suggests challenges in dataset formatting. Conclusions KoMeP is the first publicly available Korean medical preference dataset and significantly improves alignment tuning performance in LLMs. The DPO method outperforms ORPO in alignment tuning. Future work should focus on expanding KoMeP, developing a Korean-native dataset, and refining alignment tuning methods to produce safer and more reliable Korean medical LLMs.

Purpose: To report a case of paraneoplastic autoimmune retinopathy in a patient with a history of testicular cancer.Case summary: A 29-year-old man presented with photopsia and floaters. Initial fundus examination revealed no abnormal findings. However, he returned 1 month later with complaints of visual field defects. Fundus examination revealed diffuse white spots in the macula and midperipheral retina and fundus autofluorescence demonstrated hyper-autofluorescence. Optical coherence tomography showed disruption of the ellipsoid zone sparing the fovea. Visual field examination revealed peripheral visual field defects and an electroretinogram showed reduced rod and cone cell responses. Considering his history of testicular cancer, serum paraneoplastic autoantibody panel testing was performed which revealed borderline levels of anti-recoverin antibody leading to a diagnosis of paraneoplastic autoimmune retinopathy. The patient was treated with oral steroids and mycophenolate mofetil for 1 year. However, there was no improvement in the subjective symptoms or ophthalmologic findings. Conclusions This case of paraneoplastic autoimmune retinopathy in a young man with a history of testicular cancer highlights an early clinical presentation of the disease. It is crucial to recognize that the initial clinical presentation of autoimmune retinopathy can be nonspecific.

Purpose: To report a case of paraneoplastic autoimmune retinopathy in a patient with a history of testicular cancer.Case summary: A 29-year-old man presented with photopsia and floaters. Initial fundus examination revealed no abnormal findings. However, he returned 1 month later with complaints of visual field defects. Fundus examination revealed diffuse white spots in the macula and midperipheral retina and fundus autofluorescence demonstrated hyper-autofluorescence. Optical coherence tomography showed disruption of the ellipsoid zone sparing the fovea. Visual field examination revealed peripheral visual field defects and an electroretinogram showed reduced rod and cone cell responses. Considering his history of testicular cancer, serum paraneoplastic autoantibody panel testing was performed which revealed borderline levels of anti-recoverin antibody leading to a diagnosis of paraneoplastic autoimmune retinopathy. The patient was treated with oral steroids and mycophenolate mofetil for 1 year. However, there was no improvement in the subjective symptoms or ophthalmologic findings. Conclusions This case of paraneoplastic autoimmune retinopathy in a young man with a history of testicular cancer highlights an early clinical presentation of the disease. It is crucial to recognize that the initial clinical presentation of autoimmune retinopathy can be nonspecific.

Purpose: To report a case of paraneoplastic autoimmune retinopathy in a patient with a history of testicular cancer.Case summary: A 29-year-old man presented with photopsia and floaters. Initial fundus examination revealed no abnormal findings. However, he returned 1 month later with complaints of visual field defects. Fundus examination revealed diffuse white spots in the macula and midperipheral retina and fundus autofluorescence demonstrated hyper-autofluorescence. Optical coherence tomography showed disruption of the ellipsoid zone sparing the fovea. Visual field examination revealed peripheral visual field defects and an electroretinogram showed reduced rod and cone cell responses. Considering his history of testicular cancer, serum paraneoplastic autoantibody panel testing was performed which revealed borderline levels of anti-recoverin antibody leading to a diagnosis of paraneoplastic autoimmune retinopathy. The patient was treated with oral steroids and mycophenolate mofetil for 1 year. However, there was no improvement in the subjective symptoms or ophthalmologic findings. Conclusions This case of paraneoplastic autoimmune retinopathy in a young man with a history of testicular cancer highlights an early clinical presentation of the disease. It is crucial to recognize that the initial clinical presentation of autoimmune retinopathy can be nonspecific.

Background: and Purpose Cognitive and behavioral changes are common in amyotrophic lateral sclerosis (ALS), with about 15% of patients presenting with overt frontotemporal dementia and 30%–50% with varying degrees of impairments. We aimed to develop and validate the Korean version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS-K), a brief multidomain assessment tool developed for ALS patients with physical disability. Methods: We developed the ECAS-K according to the translation guidelines, and administered it to 38 patients with ALS and 26 age- and education-level-matched controls. We also administered the Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB) to investigate convergent validity, and the Center for Neurologic Study-Liability Scale to assess the association between pseudobulbar affect and cognitive/behavioral changes. Results: Internal consistency among the ECAS-K test items was found to be high, with a Cronbach’s alpha of 0.87. Significant differences were found between patients with ALS and the controls in language, fluency, and memory functions (p<0.05). Abnormal performance based on the ECAS total score was noted in 39.4% of patients, and 66.6% presented behavioral changes in at least one domain. Significant correlations were observed between the scores of the ECAS-K and those of other cognitive screening tools (MoCA and FAB, with correlation coefficients of 0.69 and 0.55, respectively; p<0.01). Conclusions We developed and validated the ECAS-K which could be used as an effective tool to screen the cognitive and behavioral impairments in Korean patients with ALS.