News from the AI & ML world
DeeperML - #healthcareai
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@www.marktechpost.com
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OpenAI has introduced HealthBench, a new open-source benchmark designed to evaluate AI performance in realistic healthcare scenarios. Developed in collaboration with over 262 physicians, HealthBench uses 5,000 multi-turn conversations and over 48,000 rubric criteria to grade AI models across seven medical domains and 49 languages. The benchmark assesses AI responses based on communication quality, instruction following, accuracy, contextual understanding, and completeness, providing a comprehensive evaluation of AI capabilities in healthcare. OpenAI’s latest models, including o3 and GPT-4.1, have shown impressive results on this benchmark.
The most provocative finding from the HealthBench evaluation is that the newest AI models are performing at or beyond the level of human experts in crafting responses to medical queries. Earlier tests from September 2024 showed that doctors could improve AI outputs by editing them, scoring higher than doctors working without AI. However, with the latest April 2025 models, like o3 and GPT-4.1, physicians using these AI responses as a base, on average, did not further improve them. This suggests that for the specific task of generating HealthBench responses, the newest AI matches or exceeds the capabilities of human experts, even with a strong AI starting point. In related news, FaceAge, a face-reading AI tool developed by researchers at Mass General Brigham, demonstrates promising abilities in predicting cancer outcomes. By analyzing facial photographs, FaceAge estimates a person's biological age and can predict cancer survival with an impressive 81% accuracy rate. This outperforms clinicians in predicting short-term life expectancy, especially for patients receiving palliative radiotherapy. FaceAge identifies subtle facial features associated with aging and provides a quantifiable measure of biological aging that correlates with survival outcomes and health risks, offering doctors more objective and precise survival estimates.
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Hassan Shittu@Fello AI
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Nvidia is making significant strides in healthcare and AI infrastructure, particularly through the development of specialized large language models (LLMs). Their DNA LLM exemplifies this, aiming to revolutionize genomic research and drug discovery. This highlights AI's potential to transform medical science by enabling faster analysis and interpretation of biological data.
Lambda has been recognized as NVIDIA's 2025 Healthcare Partner of the Year for accelerating AI innovation in healthcare and biotech. John Snow Labs introduced the first commercially available Medical Reasoning LLM at NVIDIA GTC, optimized for clinical reasoning and capable of verbalizing its thought processes. Nvidia's involvement in this has helped lead the way for these healthcare specific Large Language Models.
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Ben Lorica@Gradient Flow
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DeepSeek is making significant strides in the AI landscape, particularly within the healthcare sector in China. The AI solution is being rapidly adopted across China's tertiary hospitals to improve clinical decision-making and operational efficiency. Its rollout began in Shanghai, with hospitals like Fudan University Affiliated Huashan Hospital, and has expanded nationwide. DeepSeek is being used in areas such as intelligent pathology to automate tumor analysis, imaging analysis for lung nodule differentiation, clinical decision support for evidence retrieval, and workflow optimization to reduce patient wait times.
DeepSeek has also open-sourced several code repositories to give competitors a scare on the journey toward transparency and the advancement of the AI community. This move puts the firm ahead of the competition on model transparency and the open source nature allows hospitals to customize the programs. This level of openness is a further step than other AI competitors such as Meta’s Llama, which has only open-sourced the weights of its models. DeepSeek's deployment focuses on practical applications within hospital intranets, ensuring data security while improving accuracy and generalization through hierarchical knowledge distillation, reducing computational costs.
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