Sinai School of Medicine, Stanford University and the Northern California Institute for Research and Education, IBM Research is undertaking a new research initiative funded by the National Institutes of Health. As part of a broader $99 million, 5-year research initiative spanning multiple public and private organizations and research institutions, this work will tap into AI and big data to help better identify individuals at high-risk of developing schizophrenia, a serious mental illness affecting how a person thinks, feels and behaves. Schizophrenia is often characterized by alterations to a person's thoughts, feelings and behaviors, which can include a loss of contact with reality known as psychosis. A better understanding of how this disease could be detected prior to psychosis could help to postpone or even prevent the transition to psychosis, as well as possibly improve outcomes. The project is a component of the Accelerating Medicines Partnership (AMP), a collaboration between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), pharmaceutical companies, biotech firms and nonprofit organizations.

And AI is helping healthcare professionals and patients get more insights into how they can translate what we already knew in different silos into …

The lack of proper data training for AI algorithms used for medical devices can end up being harmful to patients, experts told the FDA.

Scopio Labs, a leading provider of Full Field Morphology (FFM), announced that it was granted FDA clearance to market and sell its X100 with Full Field Peripheral Blood Smear (Full Field PBS) Application, unlocking the potential of in vitro hematology diagnosis. Full Field PBS is also available in Europe with CE mark certification granted earlier this year. Blood is one of the most foundational gateways to health information. Even with the adoption of digital tools, today's solutions do not showcase all required regions of interest in a PBS slide, only capturing snapshots of cells. To help improve diagnostic accuracy leveraging novel computer vision tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans using advanced computational photography imaging and tailored AI tools.

The U.S. Food and Drug Administration on Thursday convened a public meeting of its Patient Engagement Advisory Committee to discuss issues regarding artificial intelligence and machine learning in medical devices. "Devices using AI and ML technology will transform healthcare delivery by increasing efficiency in key processes in the treatment of patients," said Dr. Paul Conway, PEAC chair and chair of policy and global affairs of the American Association of Kidney Patients. As Conway and others noted during the panel, AI and ML systems may have algorithmic biases and lack transparency – potentially leading, in turn, to an undermining of patient trust in devices. Medical device innovation has already ramped up in response to the COVID-19 crisis, with Center for Devices and Radiological Health Director Dr. Jeff Shuren noting that 562 medical devices have already been granted emergency use authorization by the FDA. It's imperative, said Shuren, that patients' needs be considered as part of the creation process.

Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that are safe for deployment, they assume that the underlying problem is stationary. However, many real-world problems of interest exhibit non-stationarity, and when stakes are high, the cost associated with a false stationarity assumption may be unacceptable. We take the first steps towards ensuring safety, with high confidence, for smoothly-varying non-stationary decision problems. Our proposed method extends a type of safe algorithm, called a Seldonian algorithm, through a synthesis of model-free reinforcement learning with time-series analysis. Safety is ensured using sequential hypothesis testing of a policy's forecasted performance, and confidence intervals are obtained using wild bootstrap.

Aidoc announced today that the US Food and Drug Administration (FDA) has given regulatory clearance for the commercial use of its triaging and notification algorithms for flagging and communicating incidental pulmonary embolism . Flagging incidental, critical findings is a huge technical challenge due to the varied imaging protocols used and lower incidences of such cases. The ability to prioritize incidental critical conditions accurately is a breakthrough in the value AI can bring to the radiologist workflow. "The most common use case we experienced is for critical unsuspected findings in oncology surveillance patients" said Dr. Cindy Kallman, Chief, Section of CT at Cedars-Sinai Medical Center. "The ability to call the referring physician while the patient is still in the house is huge. We are essentially offering a point-of-care diagnosis of PE for our outpatients. Our referring physicians have been completely wowed by this."

For quite a while, artificial intelligence and machine learning models are leveraged in the healthcare industry to improve patient outcomes. They have been utilized in various scans, for diagnosing various diseases, for the drug manufacturing and planning the treatment for various diseases. The involvement of these AI/ML models is observed in the surgical process as well. With the amount of data being generated nowadays, the traditional AI/M- based software models are often scrutinized under the lens of performance and accuracy. As new advances are shaping the future of healthcare, the modification of the existing software models has been recognized by healthcare professionals.

For quite a while, artificial intelligence and machine learning models are leveraged in the healthcare industry to improve patient outcomes.

The findings come from a cross-sectional study, published in BMJ Open, of the comments submitted to the US Food and Drug Administration (FDA) 'Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)--Discussion Paper and Request for Feedback'. Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care, continually incorporating insights from the vast amount of data generated every day during the delivery of health care. Many such devices must have regulatory approval or clearance before being available for clinical practice, and in the US that regulation falls to the FDA. The suitability of traditional medical device regulatory pathways for AI/ML have been called into question because the nature of the technology means it is continually evolving and adapting to improve performance. Under the current framework it would mean that as devices evolved they would require further review and approval, which could be time consuming and may affect patient safety and interests.