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Neuralink has announced that the U.S. Food and Drug Administration (FDA) has approved the launch of its first clinical study in humans. "We are excited to share that we have received the FDA's approval to launch our first-in-human clinical study!" Neuralink's official Twitter account wrote on Thursday.(opens in a new tab) "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 neurotechnology company isn't recruiting test subjects just yet, and hasn't released any information on exactly what the clinical trial will involve. Even so, fans of Neuralink founder Elon Musk are already chomping(opens in a new tab) at(opens in a new tab) the(opens in a new tab) bit(opens in a new tab) to implant questionable experimental technology in their grey matter. Neuralink aims to develop implantable devices that will let people control computers with their brain, as well as restore vision or mobility to people with disabilities.

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.

Google Cloud is tackling the cumbersome problem in healthcare of slow preauthorization of recommended procedures, medications, or devices. On April 13, the company introduced its AI-enabled Claims Acceleration Suite to reduce these administrative burdens and costs for health plans and providers. The prior authorization process causes burnout for physicians, notes Amy Waldron, director of Global Health Plans Strategy and Solutions for Google Cloud. In fact, 88% of physicians call it "very or extremely" burdensome, according to the Medical Group Management Association. The Centers for Medicare & Medicaid says prior authorizations take an average of 10 days.

Should artificial intelligence or machine learning (AI/ML) be allowed to alter FDA approved software in medical devices? If so, where should the guardrails be set? The discussions and debates surrounding AI/ML are heated; some believe the technology may destroy humanity, while others look forward to the speed of advancement it will allow. The FDA is getting out ahead on this debate. This week the agency drafted a list of “guiding principles” intended to begin developing best practices for machine learning within medical devices. A new framework envisioned by the FDA includes a “predetermined change control plan” in premarket submissions. This plan would include the types of anticipated modifications, referred to as “Software as a Medical Device Pre-Specifications”. The associated methodology used to implement those changes in a measured and controlled approach that manages risk the FDA calls the “Algorithm Change Protocol.”

Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data.

This study develops a calibrated beam-based algorithm with awareness of the global attention distribution for neural abstractive summarization, aiming to improve the local optimality problem of the original beam search in a rigorous way. Specifically, a novel global protocol is proposed based on the attention distribution to stipulate how a global optimal hypothesis should attend to the source. A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion. This novel design enjoys a distinctive property, i.e., the global attention distribution could be predicted before inference, enabling step-wise improvements on the beam search through the global scoring mechanism. Extensive experiments on nine datasets show that the global (attention)-aware inference significantly improves state-of-the-art summarization models even using empirical hyper-parameters. The algorithm is also proven robust as it remains to generate meaningful texts with corrupted attention distributions. The codes and a comprehensive set of examples are available.

Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex dynamical patterns between individual time series that are hard to learn by typical generation models such as GANs. There is valuable information in those patterns that machine learning models can use to better classify, predict or perform other downstream tasks. We propose a novel framework that takes time series' common origin into account and favors channel/feature relationships preservation. The two key points of our method are: 1) the individual time series are generated from a common point in latent space and 2) a central discriminator favors the preservation of inter-channel/feature dynamics. We demonstrate empirically that our method helps preserve channel/feature correlations and that our synthetic data performs very well in downstream tasks with medical and financial data.

The Food and Drug Administration announced the availability of draft guidance that provides recommendations on lifecycle controls in submissions to market machine learning-enabled device software functions. In the "Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning-Enabled Device Software Functions," the FDA proposes to ensure that AI/ML-enabled devices "can be safely, effectively and rapidly modified, updated, and improved in response to new data," said Brendan O'Leary, deputy director of the Digital Health Center of Excellence in the FDA's Center for Devices and Radiological Health, in a March 30 announcement. FDA says that companies must also describe how information about modifications will be clearly communicated to users in the PCCP. The agency explains that control plans are not just intended for the AI/ML-enabled software as a medical device – "but for all AI/ML-enabled device software functions." "The approach FDA is proposing in this draft guidance would ensure that important performance considerations, including with respect to race, ethnicity, disease severity, gender, age and geographical considerations, are addressed in the ongoing development, validation, implementation and monitoring of AI/ML-enabled devices," he said.