Clinical trials require numerous documents to be written -- protocols, consent forms, clinical study reports and others. Large language models (LLMs) offer the potential to rapidly generate first versions of these documents, however there are concerns about the quality of their output. Here we report an evaluation of LLMs in generating parts of one such document, clinical trial protocols. We find that an offthe-shelf LLM delivers reasonable results, especially when assessing content relevance and the correct use of terminology. However, deficiencies remain: specifically clinical thinking and logic, and appropriate use of references. To improve performance, we used retrieval-augmented generation (RAG) to prompt an LLM with accurate up-to-date information. As a result of using RAG, the writing quality of the LLM improves substantially, which has implications for the practical useability of LLMs in clinical trial-related writing.

This legal action ignited a heated debate, contributing to a growing series of lawsuits against AI providers [9-11, 54]. This incident underscores the inadequacy of current AI harm reporting mechanisms, leaving small harmed parties with limited recourse unless backed by substantial legal support or media awareness, despite the recognized potential for improving AI systems by exposing issues [78]. Current AI accountability initiatives primarily rely on periodic audits, emphasizing repetitive assessments but lacking a structured reporting framework for user-identified issues post-deployment. This audit-centric paradigm is reflected in influential policies such as the U.S. Executive Order on AI [93], the EU's draft AI Act [43], and New York City's Local Law 144[69]. However, this approach falls short when compared to the more comprehensive Coordinated Vulnerability Disclosure(CVD) processes standard in software security. Coordinated Vulnerability Disclosure (CVD) plays a crucial role as a mechanism for independent researchers to report newly identified vulnerabilities to affected vendors and the public [58]. This process enables transparent remediation before potential exploitation by malicious actors and has become a vital practice enshrined in government regulations and industry standards. Notably, the FDA mandates the implementation of CVD programs for medical device companies to enhance cybersecurity[96]. While CVD has demonstrated effectiveness in traditional software security, its direct application to machine learning (ML) systems faces unique challenges.

Visit any health-care facility, and one of the first things they'll do is clip a pulse oximeter to your finger. These devices, which track heart rate and blood oxygen, offer vital information about a person's health. For people with dark skin, pulse oximeters can overestimate just how much oxygen their blood is carrying. That means that a person with dangerously low oxygen levels might seem, according to the pulse oximeter, fine. The US Food and Drug Administration is still trying to figure out what to do about this problem. Last week, an FDA advisory committee met to mull over better ways to evaluate the performance of these devices in people with a variety of skin tones.

Purpose: Large Language Models (LLMs) like ChatGPT and LLaMA are increasingly recognized for their potential in the field of cheminformatics, particularly in interpreting Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs can decode SMILES strings into vector representations, providing a novel approach to understanding chemical graphs. Methods: We investigate the performance of ChatGPT and LLaMA in embedding SMILES strings. Our evaluation focuses on two key applications: molecular property (MP) prediction and drug-drug interaction (DDI) prediction, both essential in drug development and healthcare. Results: We find that SMILES embeddings generated using LLaMA outperform those from ChatGPT in both MP and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to existing methods in both prediction tasks. Conclusion: The application of LLMs in cheminformatics, particularly in utilizing SMILES embeddings, shows significant promise for advancing drug development. This includes improving the prediction of chemical properties and facilitating the drug discovery process. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-ChatGPT

Inspire therapy is an FDA-approved internal neurostimulation treatment for obstructive sleep apnea. However, not all patients respond to this therapy, posing a challenge even for experienced otolaryngologists to determine candidacy. This paper makes the first attempt to leverage both machine learning and deep learning techniques in discerning patient responsiveness to Inspire therapy using medical data and videos captured through Drug-Induced Sleep Endoscopy (DISE), an essential procedure for Inspire therapy. To achieve this, we gathered and annotated three datasets from 127 patients. Two of these datasets comprise endoscopic videos focused on the Base of the Tongue and Velopharynx. The third dataset composes the patient's clinical information. By utilizing these datasets, we benchmarked and compared the performance of six deep learning models and five classical machine learning algorithms. The results demonstrate the potential of employing machine learning and deep learning techniques to determine a patient's eligibility for Inspire therapy, paving the way for future advancements in this field.

Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in the absence of ground truth labels to evaluate the accuracy of such models. To date, attempts at transparent causal effect estimation consist of applying post hoc explanation methods to black-box models, which are not interpretable. Here, we present BICauseTree: an interpretable balancing method that identifies clusters where natural experiments occur locally. Our approach builds on decision trees with a customized objective function to improve balancing and reduce treatment allocation bias. Consequently, it can additionally detect subgroups presenting positivity violations, exclude them, and provide a covariate-based definition of the target population we can infer from and generalize to. We evaluate the method's performance using synthetic and realistic datasets, explore its bias-interpretability tradeoff, and show that it is comparable with existing approaches.

Elon Musk's Neuralink hit the headlines this morning, after the entrepreneur announced that his firm had implanted a chip in the brain of a human for the first time. Having gained FDA approval, Musk announced that a device called'Telepathy' had been implanted and that the unnamed patient was recovering well. But after Neuralink's early trials saw 1,500 animals killed during rushed experiments, experts have raised serious concerned about the implant's safety. Speaking to MailOnline, Dr Dean Burnett, honorary research associate at Cardiff University, called the human trials'disconcerting and alarming.' 'The speed at which [Musk] has gone from having no involvement in neurosurgical implants to making massive global statements is disconcerting and alarming,' he said.

Elon Musk has left even his most ardent fans terrified after he revealed his tech start-up Neuralink has become the first to successfully implant a microchip into a human brain. The world's richest man said the operation took place on Sunday and'initial results show promising neuron spike detection'. The device - called'Telepathy' will'enable control of your phone or computer, and through them almost any device, just by thinking', he said. But many of his 170 million followers on X, formerly Twitter, accused him of'mind control', creating'cyborgs', and even'playing God'. 'The negative potential of this makes me very uneasy,' one person wrote in a reply to his announcement.

Elon Musk, Neuralink's billionaire founder, said the first human received an implant from the brain-chip startup on Sunday and is recovering well, in a post on Twitter/X on Monday. The US Food and Drug Administration (FDA) had given the company clearance last year to conduct its first trial to test its implant on humans. "Initial results show promising neuron spike detection," Musk added. The startup's Prime study is a trial for its wireless brain-computer interface to evaluate the safety of the implant and surgical robot. The study will assess the functionality of the interface, which enables people with quadriplegia, or paralysis of all four limbs, to control devices with their thoughts, according to the company's website.

The adoption of machine-learning-enabled systems in the healthcare domain is on the rise. While the use of ML in healthcare has several benefits, it also expands the threat surface of medical systems. We show that the use of ML in medical systems, particularly connected systems that involve interfacing the ML engine with multiple peripheral devices, has security risks that might cause life-threatening damage to a patient's health in case of adversarial interventions. These new risks arise due to security vulnerabilities in the peripheral devices and communication channels. We present a case study where we demonstrate an attack on an ML-enabled blood glucose monitoring system by introducing adversarial data points during inference. We show that an adversary can achieve this by exploiting a known vulnerability in the Bluetooth communication channel connecting the glucose meter with the ML-enabled app. We further show that state-of-the-art risk assessment techniques are not adequate for identifying and assessing these new risks. Our study highlights the need for novel risk analysis methods for analyzing the security of AI-enabled connected health devices.