A New York City based large volume private practice radiology group conducted a quality assurance review that included an 18 month software evaluation in the breast center comprised of nine (9) specialist radiologists using an FDA cleared artificial intelligence software by Koios Medical, Inc as a second opinion for analyzing and assessing lesions found during breast ultrasound examinations. Over the evaluation period, radiologists analyzed over 6,000 diagnostic breast ultrasound exams. Radiologists used Koios DS Breast decision support software (Koios Medical, Inc.) to assist in lesion classification and risk assessment. As part of the normal diagnostic workflow, radiologists would activate Koios DS and review the software findings with clinical details to formulate the best management. Analysis was then performed comparing the physicians' diagnostic performance to the 18-month period prior to the introduction of the AI enabled software.

Paige, a NYC-based leader in computational pathology transforming the diagnosis and treatment of cancer, today announced it has closed its Series B funding round of $45 million, bringing the Company's total capital raised to over $70 million. Healthcare Venture Partners brought the largest contribution to the round, with Breyer Capital, Kenan Turnacioglu, and other funds participating. Paige will use this new capital to drive FDA clearance of its products and expand its portfolio, delving deeper into cancer pathology, novel biomarkers, and prognostic capabilities. Additionally, the Company will accelerate commercial efforts in the U.S. and expansion in Europe, Brazil, and Canada. Pathology is the cornerstone of cancer diagnoses.

In April 2018, the US Food and Drug Administration (FDA) made a momentous decision. The agency's approval of IDx-DR, a diagnostic system developed by Iowa-based IDx Technologies for diabetic retinopathy, wasn't a revolutionary move on the face of it, but nevertheless marked an important inflection point in the delivery of modern healthcare. So why was the FDA's decision to award marketing clearance to IDx-DR so significant? As is increasingly the case in medical technology, the answer lies with artificial intelligence (AI). The IDx-DR software is driven by AI, and it's the first system approved to autonomously provide diagnostic assessments without the supervision of an expert clinician. The system involves capturing images of a patient's eye with a retinal camera – in this case the Topcon NW400 – that can be operated by any non-specialist staff member with a little training.

Artificial Intelligence (AI) has emerged as one of the most important topics in radiology today. The Association of University Radiologists (AUR9) in its role of organizing and representing the interests of academic radiologists and those of radiology at large, convened a roundtable to help radiologists and industry leaders share their points of view and their goals in order to foster a shared understanding about the impact and benefits of AI applications in the field of radiology. There is a clear mutual interdependence between the radiology community and industry partners, which, in the case of AI, should foster collaboration between the two groups. In order to advance radiological sciences and to bridge the gap between clinicians and engineers, members of both groups need to work together so as to ensure the development of common goals, shared understanding, and mutually productive efforts. This type of collaboration occurs most frequently at the local level between a single radiology academic department and a single manufacturer.

After a successful early career in R&D in Silicon Valley, I spent 12 years working as a carpenter. This may sound like a big U-turn. But, while I loved the intellectual piece of science, I really loved the people aspect of construction. I got to build something and turn raw materials into gratifying, highly visible results: houses that enabled life and buildings that enabled commerce. I get the same kind of rush daily as lead data-ops engineer for Life Sciences at Tamr.*

As we come towards the end of the year, industry experts discuss how the clinical research market has evolved and how they are looking to prepare for the challenges to overcome in 2020. The past year saw CluePoints, a software developer providing clinical trial monitoring services, build on its agreement with the US Food and Drug Administration (FDA) to provide its services supporting the regulator's oversight of the clinical trial market. Asked about how market demands have shifted since 2018, the company's CCO, Patrick Hughes pointed to the ICH E6 (R2) good clinical practice (GCP) guidance, which'became a reality' for sponsors and clinical research organizations (CROs). As a result, this made 2019, "the year in which we have seen the biggest momentum shift across the industry in the adoption of a risk-based approach to trial management," Hughes said. Risk-based quality management in clinical trials focuses on identifying the most important compliance risks in a study and setting them as a priority in order to prevent and avoid potential disruptions.

The Food and Drug Administration (FDA) is announcing the following public workshop entitled "Evolving Role of Artificial Intelligence in Radiological Imaging." The intent of this public workshop is to discuss emerging applications of Artificial Intelligence (AI) in radiological imaging including AI devices intended to automate the diagnostic radiology workflow as well as guided image acquisition. The purpose of the workshop is to work with interested stakeholders to identify the benefits and risks associated with use of AI in radiological imaging. We also plan to discuss best practices for the validation of AI-automated radiological imaging software and image acquisition devices. Validation of device performance with respect to the intended use is critical to assess safety and effectiveness.

This included the technology ProFound AI for Digital Breast Tomosynthesis (DBT), which is said to be the first artificial intelligence software for DBT to be approved by the U.S. Food and Drug Administration (FDA). Also on offer at the event were medical software solutions designed for 2D mammography and to assess breast density. During the meeting, the iCAD unveiled its vision for future technologies. This predictive aspect included technologies that should enable clinicians to more easily interpret patients' earlier images and prospective breast cancer risk assessment to form a clearer picture of the specific patient's condition. Clinical data from a large reader study involving ProFound AI for DBT were recently published in the journal Radiology: Artificial Intelligence ("Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis").

Editor's Note: This edition of Morning eHealth is published Mondays, Wednesdays and Fridays at 10 a.m. POLITICO Pro eHealth subscribers hold exclusive early access to the newsletter each morning at 6 a.m. Manage the high volume of regulatory information and track new changes and comments all in one, easy-to-use platform. I'm not able to schedule diagnostic tests online via my EHR platform. It's FRIDAY at Morning eHealth, where your author has yet to see a Baby Yoda/health IT crossover meme, which is perhaps for the best.

As use of artificial intelligence and machine learning (AI/ML) in medicine continues to grow, regulators face a fundamental problem: After evaluating a medical AI/ML technology and deeming it safe and effective, should the regulator limit its authorization to market only the version of the algorithm that was submitted, or permit marketing of an algorithm that can learn and adapt to new conditions? For drugs and ordinary medical devices, this problem typically does not arise. But it is this capability to continuously evolve that underlies much of the potential benefit of AI/ML. We address this "update problem" and the treatment of "locked" versus "adaptive" algorithms by building on two proposals suggested earlier this year by one prominent regulatory body, the U.S. Food and Drug Administration (FDA) (1, 2), which may play an influential role in how other countries shape their associated regulatory architecture. The emphasis of regulators needs to be on whether AI/ML is overall reliable as applied to new data and on treating similar patients similarly. We describe several features that are specific to and ubiquitous in AI/ML systems and are closely tied to their reliability.