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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.

The government's 10-year health plan for the NHS in England expects AI scribes to'liberate staff from their current burden of bureaucracy and administration'. The government's 10-year health plan for the NHS in England expects AI scribes to'liberate staff from their current burden of bureaucracy and administration'. Doctors' AI scribes get names of drugs and diagnoses wrong, NHS watchdog warns AI technology that listens to and transcribes patients' consultations with doctors can put them at risk by getting the names of drugs and illnesses wrong, an NHS watchdog has warned. In one case a woman was left badly shaken when the AI scribe's summary of her conversation wrongly said she had demyelination - serious nerve damage that can lead to multiple sclerosis. It was only when the patient, an NHS health professional, queried the AI tool's record of the result of her MRI scan that the hospital corrected it to what it should have been - "null demyelination".

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The US Food and Drug Administration has approved a new product aimed at automating blood draws in outpatient situations. Aletta is a standalone robotic device capable of drawing blood from a patient's arm. It uses near-infrared light and Doppler ultrasound to identify a vein and then automates the other processes of a blood draw, such as applying a tourniquet, inserting and disposing of a needle and placing a bandage on the patient. A trained phlebotomist must begin the procedure and oversee the device while it is in use, but a single person can monitor up to three Aletta robots at once. "Blood draws are one of the most commonly performed medical procedures in the United States, yet patients may face delays due to a growing shortage of trained phlebotomists." said Michelle Tarver, director of the FDA's Center for Devices and Radiological Health. The approval for Aletta was granted based on clinical data showing it was capable of successful blood draws at rates comparable to or better than a phlebotomist.

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A US Food and Drug Administration (FDA) advisory panel has voted to loosen restrictions on controversial peptides that have become popular online, but have not been well researched for human usage. The panel narrowly voted to allow specialised pharmacies to produce drugs including BPC-157, TB-500 and KPV, a major regulatory hurdle towards making them available via prescription. Peptides are small proteins that our bodies normally produce and that have long been used to treat medical conditions, including diabetes. But unregulated injectable peptides have exploded in the online wellness community - including among many young influencers - since GLP-1s (weight loss drugs) became mainstream. The FDA does not have to follow the panel's recommendations, but often does.

We examine hypothesis testing within a principal-agent framework, where a strategic agent, holding private beliefs about the effectiveness of a product, submits data to a principal who decides on approval. The principal employs a hypothesis testing rule, aiming to pick a p-value threshold that balances false positives and false negatives while anticipating the agent's incentive to maximize expected profitability. Building on prior work, we develop a game-theoretic model that captures how the agent's participation and reporting behavior respond to the principal's statistical decision rule. Despite the complexity of the interaction, we show that the principal's errors exhibit clear monotonic behavior when segmented by an efficiently computable critical p-value threshold, leading to an interpretable characterization of their optimal p-value threshold.

In settings where ML models are used to inform the allocation of resources, agents affected by the allocation decisions might have an incentive to strategically change their features to secure better outcomes. While prior work has studied strategic responses broadly, disentangling misreporting from genuine adaptation remains a fundamental challenge. In this paper, we propose a causally-motivated approach to identify and quantify how much an agent misreports on average by distinguishing deceptive changes in their features from genuine adaptation. Our key insight is that, unlike genuine adaptation, misreported features do not causally affect downstream variables (i.e., causal descendants). We exploit this asymmetry by comparing the causal effect of misreported features on their causal descendants as derived from manipulated datasets against those from unmanipulated datasets. We formally prove identifiability of the misreporting rate and characterize the variance of our estimator. We empirically validate our theoretical results using a semi-synthetic and real Medicare dataset with misreported data, demonstrating that our approach can be employed to identify misreporting in real-world scenarios.