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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.

When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The typical solution is to redesign the predictor itself to explicitly account for strategic interactions. In practice, however, decision makers are often constrained to adjusting coarser levers within existing prediction pipelines. For example, healthcare organizations often select which features to exclude based on perceived manipulability, while using standard regularization procedures to shrink the coefficients of retained features. In this work, we initiate a formal study of strategic classification through feature selection and its interaction with ridge regularization. Our main finding is that excluding individual features based on their manipulability alone is generally suboptimal. We provide a fine-grained characterization of the performance of a feature subset under optimal regularization, yielding new insights for policy design. Motivated by this characterization, we develop a practical algorithm for jointly choosing the feature set and the level of ridge regularization. Through a real-world case study on a healthcare payments benchmark, we illustrate how our algorithm can guide the design of coarse policy levers in practice. Our results provide a principled, practical framework for mitigating the effects of strategic behavior in algorithmic decision-making systems.

Large language models (LLMs) are increasingly deployed in medical contexts, raising critical concerns about safety, alignment, and susceptibility to adversarial manipulation. While prior benchmarks assess model refusal capabilities for harmful prompts, they often lack clinical specificity, graded harmfulness levels, and coverage of jailbreak-style attacks. We introduce CARES (Clinical Adversarial Robustness and Evaluation of Safety), a benchmark for evaluating LLM safety in healthcare. CARES includes over 18,000 prompts spanning eight medical safety principles, four harm levels, and four prompting styles: direct, indirect, obfuscated, and role-play, to simulate both malicious and benign use cases.

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