AI Medical Compendium

High-Throughput Prediction of Single-Point Energies for Charged Polymer Monomers through Feature-Engineered Machine Learning.

The journal of physical chemistry. B May 26, 2026

Accurate prediction of single-point energy is essential for understanding molecular stability, electronic structure, and environment-dependent energetics, particularly for polar and charged polymeric monomers. Density functional theory (DFT) provides... read more

A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition.

Chemical research in toxicology May 26, 2026

Early warning systems (EWSs) are currently being developed by various authorities aiming at identifying potentially hazardous chemicals before they become a threat to the environment and human health. In this context, patents provide an excellent dat... read more

What Do We Know About How Generative AI is Being Used for Patient-Centered Communication? A Scoping Review.

Health communication May 26, 2026

This paper offers a scoping review of extant research examining how generative artificial intelligence (AI) is being designed, tested, and used to facilitate patient-centered communication. Using the Joanna Briggs Institute methodology and PRISMA-ScR... read more

Machine learning to predict occult neck node metastasis in early oral squamous cell carcinoma - a comparison of models to risk stratify patients undergoing a sentinel lymph node biopsy.

Future oncology (London, England) May 26, 2026

BACKGROUND: Sentinel node biopsy (SNB) provides pathological staging of the neck in T1/T2 node-negative oral squamous cell carcinoma (OSCC). Up to 85% of SNB positive patients will have a negative completion neck dissection (CND), thus receiving unne... read more

A review and evaluation of internal migration forecasting models.

Population studies May 26, 2026

Recognizing that internal migration is an important source of error in population projections, we evaluate 21 forecasts from five classes of methods: (1) average of the most recent flows; (2) time-series econometric models with and without external f... read more

Crystallization-Driven Stable Resistive Switching and Reproducible Synaptic Learning in GeSe-Based Artificial Synapses.

ACS applied materials & interfaces May 26, 2026

The rising demand for high-performance, energy-efficient neuromorphic systems has driven the exploration of chalcogenide materials with tunable electronic properties. Here, Ag/GeSe/FTO resistive-switching devices are demonstrated that exhibit Pavlovi... read more

Aquifer Thermal Energy Storage: Groundwater for Efficient Data Center Cooling in the United States.

Ground water May 26, 2026

Data centers are energy end users with the fastest growing need for electricity in the United States, mainly because of the rapid expansion of cloud computing and artificial intelligence (AI). A substantial portion of this electricity, between 10% an... read more

Smart dual-wavelength UV-vis spectroscopic method and smartphone-based RGB method for the rapid quantification of pyronaridine tetraphosphate: integration with the SDG approach.

Analytical methods : advancing methods and applications May 26, 2026

The proposed study is based on the development of the UV-vis spectrum of pyronaridine tetraphosphate by AI, attention-based recurrent neural networks, DFT and in-lab experiments. This novel smart method was developed using water to maintain greenness... read more

Synthesis of Covalent Organic Frameworks and Their Applications in the Food Industry.

Journal of agricultural and food chemistry May 26, 2026

Covalent organic frameworks (COFs) are an emerging class of designable porous materials that demonstrate tremendous potential for advancing food science. This Review systematically analyzes the linkage forms and synthesis strategies of COFs, focusing... read more

Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces.

Chemical communications (Cambridge, England) May 26, 2026

Machine learning (ML) establishes a new paradigm for electrocatalyst and electrolyte research by coupling high-throughput screening (HTS) with a data-driven understanding of electrochemical surfaces and interfaces. This review presents an end-to-end ... read more

Artificial Intelligence Medical Compendium