Latest AI and machine learning research in fda general for healthcare professionals.
The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engagement, and information dissemination. Powerful generative AI (GAI) tools like ChatGPT and other similar chatbots have emerged, facilitating user interactions with virtual conversational agents. However, the increasing use of GAI tools in medical resea...
Electronic Health Records (EHRs) sampled from different populations can introduce unwanted bi-ases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this context, our main goal is to design an effective method to transfer knowledge between population groups, with computable guarantees for suitability, and that can be...
With over a thousand FDA-approved artificial intelligence/machine learning-enabled medical devices, research and publications is maturing from focusin...
Incomplete reporting of a study’s methods and results hinders efforts to evaluate and reproduce research findings in randomized controlled trials (RCT...
Although genetic variant effects often interact non-additively, strategies to uncover epistasis remain in their infancy. Here, we develop low-signal s...
Pediatric trials are ethically and logistically difficult, so the U.S. FDA often extrapolates adult data to children when justified. Yet no public res...
Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection, and impaired qualit...
The Nocturnal Glycemic Stability Index (NGSI) is a novel quantitative metric designed to comprehensively assess overnight glucose stability by integra...
The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impac...
Gadolinium-based Contrast Agents (GBCAs) are used in brain MRI exams to improve the visualization of pathology and improve the delineation of lesions....
This study aimed to evaluate the novelty and potential value of therapeutic suggestions made by an artificial intelligence large language model for tr...
Large language models (LLMs) demonstrate human-level performance in three key domains: linguistic understanding, knowledge-based reasoning, and comple...
Artificial intelligence (AI) is rapidly transforming healthcare, but its benefits are not reaching all patients equally. Children remain overlooked wi...
Alzheimer’s disease (AD), the leading cause of dementia, imposes a significant societal and economic burden; however, its complex molecular mechanisms...
Oncology drug development encounters considerable challenges due to positive Phase I trials rarely leading to regulatory approvals, extremely competit...
The last three years have seen an explosion in published manuscripts analysing open-access health datasets, in many cases presenting misleading or bio...
The rapid expansion of molecularly informed therapies in oncology, coupled with evolving regulatory FDA approvals, poses a challenge for oncologists s...
Large language models (LLMs) have fundamentally changed how patients and clinicians retrieve information; however, it is unclear how accurate and cons...
The early detection of adverse drug events (ADEs) became a critical issue in clinical research after the thalidomide disaster in 1961, which resulted ...
Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex d...