News from the AI & ML world
DeeperML - #medicalai
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Ellie Ramirez-Camara@Data Phoenix
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Abridge, a healthcare AI startup, has successfully raised $300 million in Series E funding, spearheaded by Andreessen Horowitz. This significant investment will fuel the scaling of Abridge's AI platform, designed to convert medical conversations into compliant documentation in real-time. The company's mission addresses the considerable $1.5 trillion annual administrative burden within the healthcare system, a key contributor to clinician burnout. Abridge's technology aims to alleviate this issue by automating the documentation process, allowing medical professionals to concentrate on patient care.
Abridge's AI platform is currently utilized by over 150 health systems, spanning 55 medical specialties and accommodating 28 languages. The platform is projected to process over 50 million medical conversations this year. Studies indicate that Abridge's technology can reduce clinician burnout by 60-70% and boasts a high user retention rate of 90%. The platform's unique approach embeds revenue cycle intelligence directly into clinical conversations, capturing billing codes, risk adjustment data, and compliance requirements. This proactive integration streamlines operations for both clinicians and revenue cycle management teams. According to Abridge CEO Dr. Shiv Rao, the platform is designed to extract crucial signals from every medical conversation, silently handling complexity so clinicians can focus on patient interactions. Furthermore, the recent AWS Summit in Washington, D.C., showcased additional innovative AI applications in healthcare. Experts discussed how AI tools are being used to improve patient outcomes and clinical workflow efficiency.
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@learn.aisingapore.org
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MIT researchers have uncovered a critical flaw in vision-language models (VLMs) that could have serious consequences in high-stakes environments like medical diagnosis. The study, published May 14, 2025, reveals that these AI models, widely used to analyze medical images, struggle with negation words such as "no" and "not." This deficiency causes them to misinterpret queries, leading to potentially catastrophic errors when retrieving images based on the absence of certain objects. An example provided highlights the case of a radiologist using a VLM to find reports of patients with tissue swelling but without an enlarged heart, the model incorrectly identifying reports with both conditions, leading to an inaccurate diagnosis.
Researchers tested the ability of vision-language models to identify negation in image captions and found the models often performed as well as a random guess. To address this issue, the MIT team created a dataset of images with corresponding captions that include negation words describing missing objects. Retraining a vision-language model with this dataset resulted in improved performance when retrieving images that do not contain specific objects, and also boosted accuracy on multiple choice question answering with negated captions. Kumail Alhamoud, the lead author of the study, emphasized the significant impact of negation words and the potential for catastrophic consequences if these models are used blindly. While the researchers were able to improve model performance through retraining, they caution that more work is needed to address the root causes of this problem. They hope their findings will alert potential users to this previously unnoticed shortcoming, especially in settings where these models are used to determine patient treatments or identify product defects. Marzyeh Ghassemi, the senior author, warned against using large vision/language models without intensive evaluation if something as fundamental as negation is broken.
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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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