Artificial Intelligence Medical Compendium

AIM: To compare the accuracy and design time of artificial intelligence (AI)-generated and manually designed (MD) surgical pathways for osteotomies and root-end resections in dynamic navigation (DN)-aided endodontic microsurgery (EMS). METHODOLOGY: F... read more 

OBJECTIVES: While the implications of AI for health care are frequently discussed, AI's relationship to abortion has received limited attention. This paper surveys research on AI and abortion to date, adapts AI ethics principles for abortion contexts... read more 

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As a key feedstock for sustainable bioenergy, microalgae require precise regulation of lipid synthesis and accurate detection methods. To efficiently induce lipid accumulation while minimizing chemical intervention, this study employed multi-frequenc... read more 

This work proposes a black-box purely data-driven strategy for structural damage identification concerning localized damaged regions in plate-like structures. The proposed strategy is based on Convolution Neural Networks in the framework of a supervi... read more 

Chemiluminescent (CL) biosensors have rapidly emerged as indispensable analytical tools within the multifaceted domains of pharmaceutical research, development, and clinical diagnostics. This prominence stems from their intrinsic attributes such as h... read more 

The small sphere and large margin approach (SSLM) is a representative learning algorithm for handling imbalanced data classification problems. However, it is only effective for the vector data, and not suitable for the tensor data. How to build a nov... read more 

We focus on exploring the inherent energy flow for time series prediction in this paper, i.e., we consider the inherent energy of time series data as a sequence measuring properties such as fluctuations, oscillations, and trends. Distinctive with mai... read more 

Artificial intelligence (AI)-assisted thermostability prediction of proteins can significantly alleviate the burden of mutation screening, thereby enhancing the efficiency of protein engineering. To further improve prediction accuracy and shorten the... read more 

Large language models (LLMs) require domain-specific fine-tuning for real-world deployment, yet face critical barriers of data privacy and computational constraints. Federated learning (FL) provides an indispensable solution by enabling collaborative... read more