AIMC Topic: United States

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Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

The Lancet. Digital health
BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tu...

The regulatory environment for artificial intelligence-enabled devices in the United States.

Seminars in vascular surgery
The regulatory environment in the United States has not kept pace with the rapidly developing market for artificial intelligence (AI)-enabled devices. The number of AI-enabled devices has increased year after year. All of these devices are registered...

Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery Examination? Orthopaedic Residents Versus ChatGPT.

Clinical orthopaedics and related research
BACKGROUND: Advances in neural networks, deep learning, and artificial intelligence (AI) have progressed recently. Previous deep learning AI has been structured around domain-specific areas that are trained on dataset-specific areas of interest that ...

US regulatory considerations for low field magnetic resonance imaging systems.

Magma (New York, N.Y.)
Although there has been a resurgence of interest in low field magnetic resonance imaging (MRI) systems in recent years, low field MRI is not a new concept. FDA has a long history of evaluating the safety and effectiveness of MRI systems encompassing ...

Application of robotics in abdominal organ transplantation: A bibliometric analysis.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Robotic transplant surgery has garnered worldwide attention since 2002. Discussions on this issue have led to more publications over the past decade. This study assessed global robotic organ transplantation studies using bibliometric anal...

Revenue and Cost Analysis of a System Utilizing Natural Language Processing and a Nurse Coordinator for Radiology Follow-up Recommendations.

Current problems in diagnostic radiology
Radiology reports often contain recommendations for follow-up imaging, Provider adherence to these radiology recommendations can be incomplete, which may result in patient harm, lost revenue, or litigation. This study sought to perform a revenue asse...

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Journal of hazardous materials
Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when use...

Deep learning for asbestos counting.

Journal of hazardous materials
The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untrea...

Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs.

World neurosurgery
BACKGROUND: Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual incidence rate of 3.21 per 100,000. It is the most aggressive type of diffuse glioma and has a median survival of months after treatment. This study ai...

Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration.

Biotechnology and bioengineering
Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly ben...