AI tools that are used to take medical notes from patients could miss vital information and lead to medical staff being deskilled, according to new research. The tools, called ambient AI scribes, are used by about 40% of GPs in the UK. The scribes listen to conversations between doctors and patients and convert the speech to text, generating written notes and letters. Academics at University of Edinburgh reviewed cases involving the scribes and found the tech had some positives, such as allowing clinicians to focus on complex tasks. However, they also said patients could be less forthcoming if they knew they were being recorded and found instances where doctors did not recognise their notes.

How an alliance of tech startups, MAHA operatives, and actual medical experts made millennial women the new face of hormone therapy. Lisa Schrenk didn't know it yet, as she trudged down a dirt trail last August, but her life was about to change. She'd set out late the night before with her hiking group, scrambling up Virginia's Old Rag Mountain in darkness. They reached the peak in time to watch the sun rise over the Blue Ridge range. Afterward, the women snapped photos. In one, Schrenk gazes out over the horizon, her long dark hair pulled into a ponytail. The image is deceptively triumphant. In reality, she had been contemplating suicide. Schrenk, a longtime IT specialist for the federal government, had started setting aside belongings for friends and family, organizing her financial affairs, and clearing out her office.

AI Has Human Doctors Asking: What's Left for Us? A recent paper argues that AI is often better at doctoring than doctors. Don't be fooled by the question mark in the title of an article published this month in the Journal of the American Medical Association. When the authors, including medical superstar Ezekiel Emanuel and venture capitalist Vinod Khosla, asked, " Will Autonomous AI Exceed AI-Physicians as the Best Medical Care? " they were being rhetorical. Their answer is an emphatic YES.

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Thi Kieu Khanh Ho who is studying time-series anomaly detection. We found out more about her research, and what inspired her to study AI, and what she plans to work on next. Tell us a bit about your PhD -- where are you studying, and what is the topic of your research? I am doing my PhD at McGill University and Mila - Québec AI Institute, in the Department of Electrical and Computer Engineering, supervised by Professor Narges Armanfard. My research focuses on time-series anomaly detection, the problem of teaching AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples.

Welcome to the "Temu experience of telehealth," where everyone from Grindr to MAGA influencers can open a virtual clinic selling weight loss drugs and more. This May, the digital search company JustAnswer made an odd pivot: It started selling weight loss drugs. Launching an online pharmacy to peddle GLP-1s wasn't the obvious next step for a business that offers paid guidance from experts, but chief executive Andy Kurtzig says the decision was partly driven by advice from ChatGPT and partly by avid customer interest. The number of queries related to the drugs more than doubled between 2024 and 2025, he says. Plus, it was easy to find help: A company called WhiteLabelMD handles customer service, provides software, and connects patients with clinicians who prescribe drugs like semaglutide and tirzepatide.

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED(Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMEDon real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.

This is health care's Uber moment. Every knowledge-based profession may one day reach the point when AI outperforms the human experts. In medicine, that day appeared to come in April. A group of primarily Harvard and Stanford researchers announced the results of a study that pitted ChatGPT against hundreds of physicians in a diagnostic obstacle course involving written medical mysteries and information from real-world patients. The bot had won, and the humans weren't entirely happy about it.

The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints limiting data availability. To address these issues, we present MoodAngels, the first specialized multi-agent framework for mood disorder diagnosis. Our approach combines granular-scale analysis of clinical assessments with a structured verification process, enabling more accurate interpretation of complex psychiatric data. Complementing this framework, we introduce MoodSyn, an open-source dataset of 1,173 synthetic psychiatric cases that preserves clinical validity while ensuring patient privacy. Experimental results demonstrate that MoodAngels outperforms conventional methods, with our baseline agent achieving 12.3% higher accuracy than GPT-4o on real-world cases, and our full multi-agent system delivering further improvements. Evaluation in the MoodSyn dataset demonstrates exceptional fidelity, accurately reproducing both the core statistical patterns and complex relationships present in the original data while maintaining strong utility for machine learning applications. Together, these contributions provide both an advanced diagnostic tool and a critical research resource for computational psychiatry, bridging important gaps in AI-assisted mental health assessment.

Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Standard feature attribution methods are limited by the inherent difficulty in mapping abstract waveform fluctuations to physical anatomical pathologies. To resolve this, we propose a cross-modal method that projects feature attributions from high-performance 12-lead ECG models onto the CineECG 3D anatomical space. Our study reveals that while models trained directly on CineECG signals suffer from reduced accuracy and incoherent attributions, the proposed mapping mechanism effectively recovers clinically relevant feature rankings. Validated against a ground-truth dataset of 20 cases annotated by domain experts, the mapped explanations yield a Dice score of 0.56, significantly outperforming the 0.47 baseline of standard 12-lead attributions. These findings indicate that cross-modal averaging mapping effectively filters attribution instability and improves the localization of pathological features, combining the diagnostic expressiveness of standard ECG with the intuitive clarity of anatomical visualization.

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