The Union Health Ministry is working towards using Artificial Intelligence (AI) in a safe and effective way in public health. Union Health Minister Harsh Vardhan said in Lok Sabha on July 12 that to address gaps in India's AI ecosystem and realise its economic impact, the central government has prioritised building AI technology capabilities. "The potential of AI in public health is being explored in our country. The Ministry of Health and Family Welfare (MoHFW) is working towards using AI in a safe and effective way in public health in India," he said during Question Hour. Mr. Vardhan said a few of the initiatives undertaken by the central government to use AI in public health are Imaging Biobank for cancer, for which the NITI Aayog with Department of Bio-Technology (DBT) aims to build a database of cancer-related radiology and pathology images of more than 20,000 profiles of cancer patients with focus on major cancers prevalent in India.
Koios Medical, the leader in ultrasound diagnosis decision support software, announces its second 510(k) clearance from the U.S. Food and Drug Administration (FDA). Koios DS (Decision Support) Breast 2.0 is intended for use to assist physicians analyzing breast ultrasound images and aligns a machine learning generated probability of malignancy with the appropriate BI-RADS category. This milestone is an important step in advancing the company's mission of empowering physicians to improve diagnostic accuracy. Now cleared for use at the point of care (or connected to an image viewer for studies stored on PACS), Koios Medical's advancements represent a huge leap forward in using artificial intelligence in healthcare by bringing the power of deep learning to physicians' fingertips. Koios DS Breast 2.0 represents the most advanced AI-based diagnostic technology for ultrasound image analysis to date.
The joint effort comes as health systems are stepping up adoption and investment in data analytics, including predictive analytics and AI. In recent survey of CIOs, CTOs and chief analytics officers conducted by the Deloitte Center for Health Solutions, 84% said such technology will be extremely important to their organization's strategy over the next three years. Other healthcare sectors are investing in AI as well, giving rise to potential safety, efficacy and ethical issues as the technology is more frequently used. One year ago, FDA approved the first autonomous AI diagnostic system for sale in the U.S. The cloud-based IDx-DR software detects diabetic retinopathy in images taken by retinal cameras. And in February, Verily, the life sciences arm of Google parent Alphabet, launched an eye disease screening algorithm at Aravind Eye Hospital in Madurai, India.
Often, large corporations – which may dominate a market sector like the medical imaging field, which includes computerized tomography (CT), magnetic resonance imaging (MRI) and radiography – are so large they are not able to adapt to new, even disruptive, technologies. Sometimes these are spun off into other companies, as is the case with Harris Corporation and AuthenTec, which developed the fingerprint technology used on Apple phones. Other times, a smaller, nimbler company may emerge that introduces something transformative. Such may be the case with Central Florida's Omega Medical Imaging led by Brian Fleming, which recently received FDA clearance for its FluoroShield system. EW: I want to talk about how you became CEO of Omega, but first, explain the FluoroShield system.
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.
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.
Complex social systems are composed of interconnected individuals whose interactions result in group behaviors. Optimal control of a real-world complex system has many applications, including road traffic management, epidemic prevention, and information dissemination. However, such real-world complex system control is difficult to achieve because of high-dimensional and non-linear system dynamics, and the exploding state and action spaces for the decision maker. Prior methods can be divided into two categories: simulation-based and analytical approaches. Existing simulation approaches have high-variance in Monte Carlo integration, and the analytical approaches suffer from modeling inaccuracy. We adopted simulation modeling in specifying the complex dynamics of a complex system, and developed analytical solutions for searching optimal strategies in a complex network with high-dimensional state-action space. To capture the complex system dynamics, we formulate the complex social network decision making problem as a discrete event decision process. To address the curse of dimensionality and search in high-dimensional state action spaces in complex systems, we reduce control of a complex system to variational inference and parameter learning, introduce Bethe entropy approximation, and develop an expectation propagation algorithm. Our proposed algorithm leads to higher system expected rewards, faster convergence, and lower variance of value function in a real-world transportation scenario than state-of-the-art analytical and sampling approaches.