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.

We would never allow a drug to be sold in the market without having gone through rigorous testing -- not even in the context of a health crisis like the coronavirus pandemic. Then why do we allow algorithms that can be just as damaging as a potent drug to be let loose into the world without having undergone similarly rigorous testing? At the moment, anyone can design an algorithm and use it to make important decisions about people -- whether they get a loan, or a job, or an apartment, or a prison sentence -- without any oversight or any kind of evidence-based requirement. The general population is being used as guinea pigs. Artificial intelligence is a predictive technology.

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at contributing to the current discussion by highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also deals with the equally necessary and meaningful extension of curricula in schools and universities.

The No. 1 overarching hot topic at all the medical conferences over the past couple years has been artificial intelligence (AI). What was once science fiction or far-fetched research projects are now starting to gain U.S. Food and Drug Administration (FDA) market clearance. Some AI elements are already being used without clinicians knowing it, being integrated into the backend of cardiology imaging systems and IT reporting systems to help speed workflow. However, beyond the hype of AI, there are practical concerns, including the need for validation, clinical evidence showing AI helps patient care, and the payment system based on how medicine did things 20-30 years ago needs to change. "We have a huge gap between all this AI investment and how we actually take care of patients. We need to integrate it into our care, because if it is not part of how we take care of patients, this isn't going to work," explained John Rumsfeld, M.D., Ph.D., FACC, American College Cardiology (ACC) chief innovation officer, and professor of medicine at the University of Colorado School of Medicine.

A deadly new virus circling the globe makes many people more anxious. The pandemic's psychological toll can be particularly weighty for people with an existing mental health condition. One 25-year-old on the US East Coast seeing a therapist for help with anxiety found additional support from an unexpected source: a chatbot. "Therapy twice a month was fine before, it's just now sometimes I have days where I feel like I need something extra," says the person, who identifies as gender nonbinary, and asked to remain anonymous. Their budget didn't allow more frequent therapy sessions, making them receptive when a friend mentioned Woebot, a chatbot built on Stanford research that delivers a digital version of cognitive behavioral therapy.

Caption Health, a leading medical AI company, announced that the US Food and Drug Administration (FDA) authorized marketing of Caption Guidance, software that assists medical professionals in the acquisition of cardiac ultrasound images. Caption Guidance uses artificial intelligence to provide real-time guidance and diagnostic quality assessment of images, empowering healthcare providers--even those without prior ultrasound experience--with the ability to capture diagnostic quality images. Empowering more clinicians with ultrasound image acquisition capability will bring the benefits of ultrasound to more patients, help standardize the quality of care, and help institutions realize valuable cost and time savings. Caption Guidance was authorized via the De Novo pathway, a regulatory pathway reserved for novel technologies. The granting of this De Novo is groundbreaking, as Caption Guidance is the first medical software authorized by the FDA that provides real-time AI guidance for medical imaging acquisition.

These startups are applying AI to discover new drugs, remotely monitor patients, securely transfer patient data, and more. Healthcare has become a crucial area for artificial intelligence research and applications. Startups in the space are leveraging AI technology to help individual consumers, clinicians, and hospital systems improve everything from fitness to clinical trials to diagnostics. For example, consumers are adopting virtual assistants to inquire about symptoms and using applications to track fitness metrics. Meanwhile, radiologists are using computer vision to discern between malignant and benign cells, while hospital systems are deploying AI-driven software to analyze the financial risk of individual patients on behalf of insurers.

Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Informatics in Medicine (SIIM). Not unexpectedly, artificial intelligence accounted for much discussion amid telltale cracks in its hype. Exerting pressure was an undercurrent of practicality, bubbling up in session talks by key opinion leaders (KOLs) and from the exhibit floor, where some company representatives spoke of the continuing need for artificial intelligence (AI) to demonstrate an ROI (return on investment). This doff of the hat to practicality could be seen in the format of scientific sessions, which were kicked off by luminary speakers providing the context in which to understand research data presented in follow-on talks. Other sessions featured faculty, as in the case of one about cybersecurity hosted by J. Anthony Seibert, Ph.D., an imaging physicist on the radiology faculty of the University of California in Davis.