Highlighting the company's rapid progress in the radiology space, Israeli startup Aidoc has received its third FDA clearance for its AI-based algorithm to help highlight potential instances of cervical spinal fractures. The regulatory decision comes just a few weeks after the FDA cleared the company's pulmonary embolism product. Aidoc also has approval for its algorithm for the detection of intracranial hemorrhages through CT scans. The company's cervical spinal fracture product already received approval from European regulators. Delayed diagnosis of cervical spinal fracture is a common problem in emergency rooms and can lead to potential major neurological issues including quadriplegia.

The American Medical Informatics Association wants the Food and Drug Administration to improve its conceptual approach to regulating medical devices that leverage self-updating artificial intelligence algorithms. The FDA sees tremendous potential in healthcare for AI algorithms that continually evolve--called "adaptive" or "continuously learning" algorithms--that don't need manual modification to incorporate learning or updates. While AMIA supports an FDA discussion paper on the topic released in early April, the group is calling on the agency to make further refinements to the Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). "Properly regulating AI and machine learning-based SaMD will require ongoing dialogue between FDA and stakeholders," said AMIA President and CEO Douglas Fridsma, MD, in a written statement. "This draft framework is only the beginning of a vital conversation to improve both patient safety and innovation. We certainly look forward to continuing it."

The Food and Drug Administration announced Tuesday that it is developing a framework for regulating artificial intelligence products used in medicine that continually adapt based on new data. The agency's outgoing commissioner, Scott Gottlieb, released a white paper that sets forth the broad outlines of the FDA's proposed approach to establishing greater oversight over this rapidly evolving segment of AI products. It is the most forceful step the FDA has taken to assert the need to regulate a category of artificial intelligence systems whose performance constantly changes based on exposure to new patients and data in clinical settings. These machine-learning systems present a particularly thorny problem for the FDA, because the agency is essentially trying to hit a moving target in regulating them. The white paper describes criteria the agency proposes to use to determine when medical products that rely on artificial intelligence will require FDA review before being commercialized.

This infographic, FDA approvals for artificial intelligence-based algorithms in medicine, was designed and created by The Medical Futurist to give a clear picture about the state of A.I. in medicine and healthcare with an emphasis on FDA regulations. As I couldn't find a reliable and constantly updated database of every FDA approval for artificial intelligence-based algorithms, I decided to scan through a lot of peer-reviewed papers and FDA documents to create one so you don't have to. The infographic also contains what medical specialties the algorithms are associated with (sometimes more than one), when it was approved and what its function is. If you find any inaccuracies or you think I missed one, please do let me know so we can update the infographic.

Much has been made in recent years about the explosion of artificial intelligence (AI) in radiology and how it might impact the role of radiologists themselves. But artificial intelligence is, by definition, artificial. In an itnTV video from the 2018 Radiological Society of North America (RSNA) annual meeting, ITN Contributing Editor Greg Freiherr explored how AI cannot take the place of people, but it can help people get what they need. You can view the video at https://bit.ly/2FMgDvH. "I doubt any radiologist could build an MR or a CT scanner from scratch. They probably couldn't even build it from pieces," said Bradley J. Erickson, M.D., Ph.D., chair, radiology informatics/associate chair, research-radiology at the Mayo Clinic in Rochester, Minn., in the itnTV video.

Israeli radiology startup Aidoc has received FDA clearance for its AI-based product meant to help identify potential cases of pulmonary embolism in chest CT scans. Pulmonary embolism (PE) – which occurs when a blood clot gets lodged in the lung – is considered a silent killer that causes up to 200,000 deaths a year in the United States. The condition often strikes with little to no warning and diagnosis of a case can be extremely time-sensitive. Aidoc's technology doesn't require dedicated hardware and runs continuously on hospital systems, automatically ingesting radiological images. The 70-person company focuses on workflow optimization in radiology to help triage high risk patients for additional and faster review.

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast "clinical research" versus routine "clinical care," outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures ...

These authors contributed equally to this work. Artificial intelligence (AI) researchers claim that they have made great'achievements' in clinical realms. However, clinicians point out the so-called'achievements' have no ability to implement into natural clinical settings. The root cause for this huge gap is that many essential features of natural clinical tasks are overlooked by AI system developers without medical background. In this paper, we propose that the clinical benchmark suite is a novel and promising direction to capture the essential features of the real-world clinical tasks, hence qualifies itself for guiding the development of AI systems, promoting the implementation of AI in real-world clinical practice. AI researchers claim that they have obtained many significant'achievements' in various However, in practice, most of the AI products fail to obtain approval from the Food and Drug Administration (FDA). AI devices are not qualified handling high-risk tasks such as clinical diagnosis .

AstraZeneca announced a long-term collaboration deal with BenevolentAI, a UK-based company focused on combining computational medicine and advanced artificial intelligence. The two companies will focus on using AI and machine learning to discover and develop new drugs for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF). "The vast amount of data available to research scientists is growing exponentially each year," stated Mene Pangalo, AstraZeneca's executive vice president and president BioPharmaceuticals R&D. "By combining AstraZeneca's disease area expertise and large, diverse datasets with BenevolentAI's leading AI and machine learning capabilities, we can unlock the potential of this wealth of data to improve our understanding of complex disease biology and identify new targets that could treat debilitating diseases." No financial details were disclosed.

When Merdis Wells visited the diabetes clinic at the University Medical Center in New Orleans about a year ago, a nurse practitioner checked her eyes to look for signs of diabetic retinopathy, the most common cause of blindness. At her next visit, in February of this year, artificial intelligence software made the call. The clinic had just installed a system that's designed to identify patients who need follow-up attention. The Food and Drug Administration cleared the system -- called IDx-DR -- for use in 2018. The agency said it was the first time it had authorized the marketing of a device that makes a screening decision without a clinician having to get involved in the interpretation.