Brain-computer interface company Neuralink announced on 25 May that it has received approval from the US Food and Drug Administration (FDA) for a clinical study in humans. Neuralink made the announcement on Twitter: "We are excited to share that we have received the FDA's approval to launch our first-in-human clinical study." The tweet said that the approval "represents an important first step that will one day allow our technology to help many people". The firm also said that the recruitment is not yet open for the trial, and it has yet to give any further details about what the trial will entail. Neuralink was formed in 2016 by Elon Musk and a group of scientists and engineers with the ultimate aim of making devices that interface with the human brain – both reading information from neurons as well as feeding information directly back into the brain.

Neuralink Corp., Elon Musk's brain-implant company, said it received approval from the US Food and Drug Administration to conduct human clinical trials. "This is the result of incredible work by the Neuralink team in close collaboration with the FDA and represents an important first step that will one day allow our technology to help many people," the company said Thursday in a tweet. The FDA and Neuralink did not immediately respond to requests for comment. Musk's startup is developing a small device that will link the brain to a computer, consisting of electrode-laced wires. Placing the device requires drilling into the skull. The approval "is really a big deal," said Cristin Welle, a former FDA official and an associate professor of neurosurgery and physiology at the University of Colorado.

United States regulators have given approval for Elon Musk's start-up Neuralink to test its brain implants on people. Neuralink said on Thursday that it received clearance from the US Food and Drug Administration (FDA) for the first human clinical study of implants which are intended to let the brain interface directly with computers. "We are excited to share that we have received the FDA's approval to launch our first-in-human clinical study," Neuralink said in a post on Twitter – which is owned by Musk. Neuralink prototypes, which are the size of a coin, have so far been implanted in the skulls of monkeys, demonstrations by the startup showed. With the help of a surgical robot, a piece of the skull is replaced with a Neuralink disk, and its wispy wires are strategically inserted into the brain, an early demonstration showed.

Neuralink, Elon Musk's brain-implant company, said on Thursday it had received a green light from the US Food and Drug Administration (FDA) to kickstart its first in-human clinical study, a critical milestone after earlier struggles to gain approval. Musk has predicted on at least four occasions since 2019 that his medical device company would begin human trials for a brain implant to treat severe conditions such as paralysis and blindness. Yet the company, founded in 2016, only sought FDA approval in early 2022 – and the agency rejected the application, seven current and former employees told Reuters in March. The FDA had pointed out several concerns to Neuralink that needed to be addressed before sanctioning human trials, according to the employees. Major issues involved the lithium battery of the device, the possibility of the implant's wires migrating within the brain and the challenge of safely extracting the device without damaging brain tissue.

Turns out Elon Musk's FDA prediction was only off by about a month. After reportedly denying the company's overtures in March, the FDA approved Neuralink's application to begin human trials of its prototype Link brain-computer interface (BCI) on Thursday. Founded in 2016, Neuralink aims to commercialize BCIs in wide-ranging medical and therapeutic applications -- from stroke and spinal cord injury (SCI) rehabilitation, to neural prosthetic controls, to the capacity "to rewind memories or download them into robots," Neuralink CEO Elon Musk promised in 2020. BCIs essentially translate the analog electrical impulses of your brain (monitoring it using hair-thin electrodes delicately threaded into that grey matter) into the digital 1's and 0's that computers understand. Since that BCI needs to be surgically installed in a patient's noggin, the FDA -- which regulates such technologies -- requires that companies conduct rigorous safety testing before giving its approval for commercial use.

Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text. Briefly, we detect the molecule names in each sequence and replace them to the corresponding SMILES. In this way, the SMILES could leverage the information from surrounding text, and vice versa. The above wrapped sequences, text sequences from PubMed and SMILES sequences from PubChem are all fed into a language model for pre-training. Experimental results demonstrate that MolXPT outperforms strong baselines of molecular property prediction on MoleculeNet, performs comparably to the best model in text-molecule translation while using less than half of its parameters, and enables zero-shot molecular generation without finetuning.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. California's reparations task force is recommending as part of its set of proposals to make amends for slavery and anti-Black racism that state lawmakers address what it calls "racially biased" artificial intelligence used in health care. The task force, created by state legislation signed by Gov. Gavin Newsom in 2020, formally approved last weekend its final recommendations to the California Legislature, which will decide whether to enact the measures and send them to the governor's desk to be signed into law. The recommendations include several proposals related to health care, including some concerning medical artificial intelligence (AI), which the task force describes as "racially biased" and contributing to alleged systemic racism against Black Californians.

Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governance surrounding its use and integration is often poorly understood. What the Health AI Partnership (HAIP) aims to do in this research is to better define the requirements for adequate organizational governance of AI systems in healthcare settings and support health system leaders to make more informed decisions around AI adoption. To work towards this understanding, we first identify how the standards for the AI adoption in healthcare may be designed to be used easily and efficiently. Then, we map out the precise decision points involved in the practical institutional adoption of AI technology within specific health systems. Practically, we achieve this through a multi-organizational collaboration with leaders from major health systems across the United States and key informants from related fields. Working with the consultancy IDEO [dot] org, we were able to conduct usability-testing sessions with healthcare and AI ethics professionals. Usability analysis revealed a prototype structured around mock key decision points that align with how organizational leaders approach technology adoption. Concurrently, we conducted semi-structured interviews with 89 professionals in healthcare and other relevant fields. Using a modified grounded theory approach, we were able to identify 8 key decision points and comprehensive procedures throughout the AI adoption lifecycle. This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States. We hope these findings can inform future efforts to build capabilities to promote the safe, effective, and responsible adoption of emerging technologies in healthcare.

The USA Food and Drug Administration has accorded increasing importance to patient-reported problems in clinical and research settings. In this paper, we explore one of the largest online datasets comprising 170,141 open-ended self-reported responses (called "verbatims") from patients with Parkinson's (PwPs) to questions about what bothers them about their Parkinson's Disease and how it affects their daily functioning, also known as the Parkinson's Disease Patient Report of Problems. Classifying such verbatims into multiple clinically relevant symptom categories is an important problem and requires multiple steps - expert curation, a multi-label text classification (MLTC) approach and large amounts of labelled training data. Further, human annotation of such large datasets is tedious and expensive. We present a novel solution to this problem where we build a baseline dataset using 2,341 (of the 170,141) verbatims annotated by nine curators including clinical experts and PwPs. We develop a rules based linguistic-dictionary using NLP techniques and graph database-based expert phrase-query system to scale the annotation to the remaining cohort generating the machine annotated dataset, and finally build a Keras-Tensorflow based MLTC model for both datasets. The machine annotated model significantly outperforms the baseline model with a F1-score of 95% across 65 symptom categories on a held-out test set.

The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL for developing effective dosing policies without the need for potentially dangerous patient interaction during training. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of the 30 virtual patients available within the FDA-approved UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the total training samples required by online RL to achieve stable performance, this work shows that offline RL can significantly increase time in the healthy blood glucose range from 61.6 +\- 0.3% to 65.3 +/- 0.5% when compared to the strongest state-of-art baseline (p < 0.001). This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging control scenarios such as incorrect bolus dosing, irregular meal timings and compression errors.