FDA General

Considerations on the regulation of AI systems in the financial sector by the AI Act.

The proposal for the Artificial Intelligence regulation in the EU (AI Act) is a horizontal legal instrument that aims to regulate, according to a tailored risk-based approach, the development and use of AI systems across a plurality of sectors, including the financial sector. In particular, AI systems intended to be used to evaluate the creditworthiness or establish the credit score of natural per...

Artificial intelligence-based language learning: illuminating the impact on speaking skills and self-regulation in Chinese EFL context.

INTRODUCTION: This study investigated the effectiveness of artificial intelligence-based instruction in improving second language (L2) speaking skills and speaking self-regulation in a natural setting. The research was conducted with 93 Chinese English as a foreign language (EFL) students, randomly assigned to either an experimental group receiving AI-based instruction or a control group receiving...

Integrated Molecular Modeling and Machine Learning for Drug Design.

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug...

Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Environmental regulation is pivotal in mitigating environmental risks and promoting sustainable development, yet regulators frequently encounter resou...

Projected Growth in FDA-Approved Artificial Intelligence Products Given Venture Capital Funding.

PURPOSE: Medical imaging accounts for 85% of digital health's venture capital funding. As funding grows, it is expected that artificial intelligence (...

Aggregate Formation and Antibody Stability in Infusion Bags: The Impact of Manual and Robotic Compounding of Monoclonal Antibodies.

Monoclonal antibodies (mAbs) can be damaged during the aseptic compounding process, with aggregation being the most prevalent form of degradation. Pro...

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. How...

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signal...

Open-Source 3D Morphing Software for Facial Plastic Surgery and Facial Landmark Detection Research and Open Access Face Data Set Based on Deep Learning (Artificial Intelligence) Generated Synthetic 3D Models.

The scarcity of 3D facial models presents a significant hurdle for researchers and educators. Gathering such data demands substantial resources. To ...

Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing.

This paper discusses the challenges of producing CAR-T cells for cancer treatment and the potential for Artificial Intelligence (AI) for its improveme...

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grad...

Multicentric Ga-PSMA PET radiomics for treatment response assessment of Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer.

OBJECTIVE: The treatment with Lutetium PSMA (Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been appro...

Navigating the AI Revolution: The Case for Precise Regulation in Health Care.

Health care is undergoing a profound transformation through the integration of artificial intelligence (AI). However, the rapid integration and expans...

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. M...

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has dev...

Antimicrobial treatment imprecision: an outcome-based model to close the data-to-action loop.

Health-care systems, food supply chains, and society in general are threatened by the inexorable rise of antimicrobial resistance. This threat is driv...

The 510(k) Third Party Review Program: Promise and Potential.

Every year, the Food and Drug Administration (FDA) clears approximately 3,000 medical devices for marketing via the 510(k) pathway. These constitute 9...

Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on -mediated lipid metabolism.

Deep learning is evolving in nutritional epidemiology to address challenges including precise nutrition and data-driven disease modeling. Fermented da...

Non-destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review.

The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the ...

FDA Modernization Act 2.0: An insight from nondeveloping country.

Animal testing is required in drug development research and is crucial for assessing the efficacy and safety of medications before they are commercial...