Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of "conventional medicine" is closer than we realize, with patients seeing a computer first and subsequently a doctor.
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http://dx.doi.org/10.3390/healthcare10040608 | DOI Listing |
J Prosthodont
January 2025
Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan.
Purpose: This study aims to evaluate the effectiveness of a case-based reasoning (CBR) system in predicting the design of definitive obturator prostheses for maxillectomy patients.
Materials And Methods: Data from 209 maxillectomy cases, including extraoral images of obturator prostheses and occlusal images of maxillectomy defects, were collected from Institute of Science Tokyo Hospital. These cases were organized into a structured database using Python's pandas library.
Eur J Nucl Med Mol Imaging
January 2025
Huashan Hospital and Human Phenome Institute, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Objective: This study aims to conduct a bibliometric analysis to explore research trends, collaboration patterns, and emerging themes in the PET/MR field based on published literature from 2010 to 2024.
Methods: A detailed literature search was performed using the Web of Science Core Collection (WoSCC) database with keywords related to PET/MR. A total of 4,349 publications were retrieved and analyzed using various bibliometric tools, including VOSviewer and CiteSpace.
J Med Chem
January 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
The tedious synthesis and limited throughput biological evaluation remain a great challenge for discovering new proteolysis targeting chimera (PROTAC). To rapidly identify potential PROTAC lead compounds, we report a platform named Auto-RapTAC. Based on the modular characteristic of the PROTAC molecule, a streamlined workflow that integrates lab automation with "click chemistry" joint building-block libraries was constructed.
View Article and Find Full Text PDFNeurorehabil Neural Repair
January 2025
Department of Mental and Physical Health and Preventive Medicine, University of Campania Luigi Vanvitelli, Naples, Italy.
Background And Objective: The metaverse refers to a digital realm accessible via internet connections using virtual reality and augmented reality glasses for promoting a new era of social rehabilitation. It represents the next-generation mobile computing platform expected to see widespread utilization in the future. In the context of rehabilitation, the metaverse is envisioned as a novel approach to enhance the treatment of human functioning exploiting the "synchronized brains" potential exacerbated by social interactions in virtual scenarios.
View Article and Find Full Text PDFNonlinear Dynamics Psychol Life Sci
January 2025
Adelphi University, Garden City, NY.
We model an adaptive agent-based environment using selfish algorithm agents (SA-agents) that make decisions along three choice dimensions as they play the multi-round prisoner's dilemma game. The dynamics that emerge from mutual interactions among the SA-agents exhibit two collective-level properties that mirror living systems, thus making these models suitable for societal/biological simulation. The properties are: emergent intelligence and collective agency.
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