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豆蔻妇科大模型再突破:钉钉行业训练平台+精标数据SFT ,准确率从 77.1%上升至 90.2%
Tai Mei Ti A P P· 2025-07-10 07:49
Core Insights - The article discusses the limitations of general large language models in clinical scenarios, particularly in providing accurate medical diagnoses, highlighting the need for specialized training methods like supervised fine-tuning (SFT) [1][2][3] - The performance of the Doukou Gynecology model improved significantly from an initial accuracy of 77.1% to 90.2% through targeted SFT processes [1][3] Data Quality Control - The training dataset underwent a rigorous selection process involving systematic data cleaning, ensuring consistency between reasoning and results, and verifying the logical integrity of the data [2][5] - Low-quality data, such as those with clear medical inconsistencies, were excluded to maintain high standards [2] Model Training Phases - The first phase involved building a foundational SFT model using 1,300 meticulously labeled gynecological consultation data, achieving an initial accuracy of 77.1% [3] - The second phase focused on synthesizing symptom data and refining the model, resulting in a final diagnostic accuracy of 90.2% for six major gynecological symptoms [3][6] Iterative Optimization - Continuous iterative optimization was implemented, where high-quality samples scoring above 8 were added to the training set for further SFT, creating a cycle of training, evaluation, and retraining [10][18] - Key performance indicators were monitored throughout the process to ensure comprehensive model improvement [10] Evaluation System - A dual evaluation system was established, combining automated assessments with manual reviews by medical experts to ensure diagnostic accuracy [11][13] - The automated evaluation system utilized a high-performance language model to objectively score outputs based on a structured framework [11] Challenges and Lessons Learned - Initial reliance on manual labeling slowed data accumulation and increased costs, prompting a shift to a more efficient "machine distillation → expert review → post-training evaluation" system [14][15] - The model's ability to recognize rare diseases was enhanced through balanced sampling strategies [15] Future Directions - The company plans to explore a collaborative training paradigm combining SFT and reinforcement learning (RL) to enhance clinical reasoning capabilities [18]
钉钉上跑出的第一个行业专属大模型落地:准确率超 90% 的妇科专业大模型
AI前线· 2025-07-10 07:41
作者 | 褚杏娟 近日,钉钉企业专属 AI 平台上成功训练出了首个高准确度、高实用性的专业领域大模型——由壹生 检康 (杭州) 生命科技有限公司研发的"豆蔻妇科大模型",其在专业测试中准确率达 90.2%。 钉钉方面表示,妇科大模型的落地,意味着钉钉生态已经从 SaaS 生态、服务商生态、咨询生态、 交付生态,拓展到 AI 创业者。 与专业医生诊断吻合度达 90.2% 当前,各行各业都在努力将大模型与自身业务场景深度融合,打造行业或专业大模型,实现运营管理 的降本增效。 壹生检康是一家深耕女性精准检测及健康服务的生命科技公司,创业团队大多来自知名互联网企业、 妇产科医疗机构、生物医药公司。基于技术趋势和行业判断,王强宇团队认为,通过训练妇科专业大 模型打造 AI 医生,将有效缓解专业妇科医生、医疗服务不足的难题,对医美机构和女性用户都会带 来巨大的行业和社会价值。 专业性强的"妇科 AI 医生"并不是采用通用大模型就能简单训练出来。启动豆蔻妇科大模型研发以 来,壹生检康团队以开源大模型为基础,通过行业数据训练,第一个版本将模型诊断准确率做到 77.1% 左右。"77.1% 的准确率虽达到行业基础标准,但对于直 ...
四个理工男“硬刚”妇科诊断推理大模型,更小参数量实现更高准确率
Tai Mei Ti A P P· 2025-04-29 02:22
Core Insights - The article discusses the "resource misalignment battle" in the AI sector, where large companies focus on parameter upgrades while smaller startups target niche markets that larger firms overlook [1] - The medical industry is highlighted as a high-risk area with stringent accuracy requirements, making it difficult for general models to meet specific needs [1] - There is a growing recognition among AI companies of the importance of specialized models in vertical fields, particularly in healthcare [1] Industry Analysis - The medical field requires vertical models to achieve higher accuracy, with general models only reaching a passing score [1][2] - The relationship between general and vertical models is likened to that of a medical student and a specialized doctor, emphasizing the need for extensive practical experience [2] - Companies like 壹生检康 are focusing on developing specialized models to address the limitations of general models in specific medical scenarios [4][5] Model Development - 壹生检康 has been developing a gynecological vertical model, selecting a 32B parameter model as the optimal balance between computational resources and response effectiveness [5][7] - The training process involved multiple rounds, with the first round yielding a 50% accuracy rate, which improved to 77.1% after addressing data imbalance issues [6][13] - The final model demonstrated superior performance compared to existing models, particularly in diagnosing specific gynecological conditions [13][14] Application and Impact - The gynecological model aims to provide precise and professional services to end-users, addressing common health issues faced by young women [18] - The model is also designed to empower healthcare providers in resource-limited settings, enabling them to offer reliable gynecological consultations [18] - The use of reinforcement learning is suggested as a future direction to enhance the model's capabilities and expand its application to other medical fields [19]