InMD-X: Large Language Models for Internal Medicine Doctors
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913237440659456 |
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| author | Gwon, Hansle Ahn, Imjin Jung, Hyoje Kim, Byeolhee Kim, Young-Hak Jun, Tae Joon |
| author_facet | Gwon, Hansle Ahn, Imjin Jung, Hyoje Kim, Byeolhee Kim, Young-Hak Jun, Tae Joon |
| contents | In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking development in natural language processing, offering a suite of language models fine-tuned for various aspects of the internal medicine field. These models encompass a wide range of medical sub-specialties, enabling IMDs to perform more efficient and accurate research, diagnosis, and documentation. InMD-X's versatility and adaptability make it a valuable tool for improving the healthcare industry, enhancing communication between healthcare professionals, and advancing medical research. Each model within InMD-X is meticulously tailored to address specific challenges faced by IMDs, ensuring the highest level of precision and comprehensiveness in clinical text analysis and decision support. This paper provides an overview of the design, development, and evaluation of InMD-X, showcasing its potential to revolutionize the way internal medicine practitioners interact with medical data and information. We present results from extensive testing, demonstrating the effectiveness and practical utility of InMD-X in real-world medical scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InMD-X: Large Language Models for Internal Medicine Doctors Gwon, Hansle Ahn, Imjin Jung, Hyoje Kim, Byeolhee Kim, Young-Hak Jun, Tae Joon Computer Vision and Pattern Recognition In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking development in natural language processing, offering a suite of language models fine-tuned for various aspects of the internal medicine field. These models encompass a wide range of medical sub-specialties, enabling IMDs to perform more efficient and accurate research, diagnosis, and documentation. InMD-X's versatility and adaptability make it a valuable tool for improving the healthcare industry, enhancing communication between healthcare professionals, and advancing medical research. Each model within InMD-X is meticulously tailored to address specific challenges faced by IMDs, ensuring the highest level of precision and comprehensiveness in clinical text analysis and decision support. This paper provides an overview of the design, development, and evaluation of InMD-X, showcasing its potential to revolutionize the way internal medicine practitioners interact with medical data and information. We present results from extensive testing, demonstrating the effectiveness and practical utility of InMD-X in real-world medical scenarios. |
| title | InMD-X: Large Language Models for Internal Medicine Doctors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.11883 |