Adverse events are a serious issue in drug development and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach does not match to the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not clear due to the lack of the comparable studies. To understand the differences, we compared the model performance between the time and random splits using nine types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and strongly suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events. We provide analysis code and datasets used in the present study (https://github.com/mizuno-group/AE_prediction).

Synchron, a brain-computer interface startup, reportedly implanted its first device in a US patient earlier this month -- overtaking Elon Musk's Neuralink for the third time. The startup implanted a 1.5-inch device into the brain of an ALS patient at Mount Sinai West medical center in New York on July 6, Bloomberg first reported. A spokesperson from Synchron did not immediately respond to a request for comment. The purpose of the device is to allow the patient to communicate -- even after they have lost the ability to move -- by using their thoughts to send emails and texts. Bloomberg reported that Synchron has already implanted the device in four patients in Australia who have been able to use the brain implant to send messages on WhatsApp and shop online.

Offering the potential of enhanced resolution with accelerated scan times for magnetic resonance imaging (MRI), SmartSpeed (Philips), an emerging artificial intelligence (AI)-enabled software, has garnered FDA 510(k) clearance. In comparison to other MRI modalities, Philips said the addition of SmartSpeed to the company's Compressed SENSE MR acceleration engine offers a threefold reduction in MRI scanning time and increases image resolution up to 65 percent. "Philips' AI-based SmartSpeed reconstruction is the new benchmark among acceleration techniques for us. It improves on the company's existing Compressed SENSE (MR acceleration engine) in all aspects and allows a reduction in scan times with excellent image quality and diagnostic confidence," noted Grischa Bratke, MD, who is affiliated with the Department of Radiology at the University Hospital of Cologne in Germany. Philips noted that application of the AI reconstruction algorithm with SmartSpeed at the front end of the MR signal facilitates a high signal-to-noise ratio that enhances image quality and enables small lesion detection.

Michelle Wu, cofounder and CEO and KK (Qiang Kou 寇强) tech cofounder at Nyquist Data, an AI powered ... [ ] cloud-based platform providing business, clinical, and regulatory intelligence and analytics for medical devices and pharmaceuticals companies How long does it take to get FDA approval for a heart-failure drug? It sounds like a simple question, but without the help of an artificial intelligence (AI) powered MedTech cloud-based platform, it could take months and millions of dollars to find out. The market size for AI in healthcare is projected to reach $187.95 billion by 2030, according to Precedence Research. When Michelle Wu was first asked this question, global clinical and regulatory healthcare information was publicly available, but it was scattered around the world in different databases and languages. Worse yet, keywords were misspelled or there were handwritten notes included in the databases, making what should be searchable unsearchable.

An artificial intelligence-powered system newly cleared by the FDA, … Using its deep learning AI models and algorithms, Eye-Sync was able to …

Royal Philips, a global leader in health technology, announced its SmartSpeed artificial intelligence (AI) powered MR acceleration software has received U.S. Food and Drug Administration (FDA) 510(k) clearance. Adding advanced AI data collection algorithms to Philips' existing Compressed SENSE MR acceleration engine, Philips SmartSpeed delivers higher image resolution with 3 times faster scan times [1] and virtually no loss in image quality, representing a major step forward in diagnostic confidence and MR department productivity. With personalized treatment for complex diseases such as cancer increasing the need for high-confidence precision diagnoses, coupled with soaring caseloads due to aging populations and high levels of clinician burnout, radiology departments are under increasing pressure to improve performance, productivity, and profitability. "Philips' AI-based SmartSpeed reconstruction is the new benchmark among acceleration techniques for us. It improves on the company's existing Compressed SENSE in all aspects and allows a reduction in scan times with excellent image quality and diagnostic confidence," said Dr. Grischa Bratke, radiologist and expert in musculoskeletal imaging at the University Hospital of Cologne, Cologne, Germany.

Overjet announced today it has partnered with Affinity Dental Management to provide its practices with Overjet's AI-powered radiograph analysis solution designed to help drive optimal patient care, clinical excellence, and practice efficiency. Overjet is the only dental AI company that is FDA-cleared for both quantifying bone level measurements to aid in the diagnosis of periodontal disease and for detecting and outlining caries (cavities) on X-rays. "As clinicians, we are trained to utilize radiographs to help us find and visualize dental disease or anomalies," said Dr. Mariz Tanious, Dental Director for Affinity Dental Management. "Overjet is a tool that adds quantification and will assist in identifying areas that may have potential dental disease." "As a company, we want to stay on the cutting edge of dentistry by implementing technology in our offices," Dr. Tanious said.

When the ER gets slammed, AI triage tools are designed to help flag patients who need critical care and might otherwise be missed, flagging the most serious cases and prioritizing them for care. The first major clinical application of AI triage tools has been in radiology; companies including RapidAI, Viz.ai, and Arterys all have FDA approval for algorithms that detect signs of strokes, brain bleeds, and pulmonary embolisms from CT scans. Imagen's FDA-approved OsteoDetect analyzes wrist X-rays to detect distal radius fractures, one of the most common injuries to the joint. Mednition's real-time triage-guidance tool, KATE, analyzes EHR data and patient vitals collected at intake to help emergency nurses spot warning signs of sepsis, which accounts for more than 50% of hospital deaths. It is being used throughout the Adventist Health system and others to head off ER admissions through earlier treatment.

In a recent study published in the American Journal of Gastroenterology, researchers at Cedars-Sinai Medical Center in the United States evaluated an artificial intelligence (AI)-based smartphone application (app) trained to assess a patient's stool characteristics. Study: A Smartphone Application Using Artificial Intelligence Is Superior To Subject Self-Reporting When Assessing Stool Form. Functional gastrointestinal (GI) disorders, especially luminal ones, require that a patient self-report stool form and frequency. However, since the symptoms of diarrhea common in irritable bowel syndrome with diarrhea (IBS-D) patients are subjective, the inability to accurately report or assess stool form and frequency makes it challenging to determine the effectiveness of therapeutic interventions in these conditions. The Bristol Stool Scale (BSS) is the United States Food and Drug Administration (US-FDA) approved 7-point scale that ranks stool consistency from 1 (hard lumps) to 7 (liquid).

Researchers have noted that traumatic fractures are among the most commonly missed diagnoses.1,2 However, a new study suggests that artificial intelligence (AI) may have significant benefit in improving the assessment of fractures.3 In the study of 500 patients (268 men and 232 women), researchers compared unassisted assessment of acute fractures versus assessment with the assistance of an FDA-cleared algorithm (Boneview, Gleamer) and stand-alone use of AI. The authors found that AI assisted assessment had a 20 percent higher sensitivity (86 percent) of diagnosing fractures on radiographs in comparison to unassisted assessment (66 percent). The use of AI assistance led to a lower number of false negatives (26) in comparison to unassisted radiograph assessment (64), according to the study.