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Topic Review
Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care
Primary care stands as a cornerstone in healthcare, serving as the first point of contact and managing the most significant number of patients in the United States and worldwide. AI can mimic human reasoning and behavior and handle the increasing volume of medical data within healthcare systems. Machine learning (ML) is the most common AI technique used.
  • 56
  • 27 Mar 2024
Topic Review
Retrieval-Augmented Generation with Large Language Models in Nephrology
The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations—outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. 
  • 85
  • 18 Mar 2024
Topic Review
Role of Metabolic Connectome in Complex Diseases
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism’s phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. 
  • 121
  • 02 Feb 2024
Topic Review
Large Language Models and Application in Nephrology
Large language models (LLMs), such as GPT-4, are an emergent technology that uses machine learning to process and analyze human language. Initially designed to improve natural language understanding and generation, LLMs have begun to extend their applicability beyond text-based tasks like translation, summarization, or conversational agents. Chain-of-thought prompting can enhance the problem-solving capabilities of AI models, particularly in complex and nuanced fields like medicine. 
  • 93
  • 17 Jan 2024
Topic Review
Virtual Reality for Rehabilitation of Acquired Cognitive Disorders
Virtual Reality (VR) is presented as a transformative tool that immerses individuals in interactive environments, offering promising opportunities for enhancing cognitive functions and improving quality of life.
  • 107
  • 05 Jan 2024
Topic Review
Apps in Anesthesia
Modern anesthesia continues to be impacted in new and unforeseen ways by digital technology. Combining portability and versatility, mobile applications or “apps” provide a multitude of ways to enhance anesthetic and peri-operative care.
  • 289
  • 30 Nov 2023
Topic Review
AI in Thyroid Cancer Diagnosis
Artificial intelligence (AI) exceptional capabilities, including pattern recognition, predictive analytics, and decision-making skills, enable the development of systems that can analyze complex medical data at a scale and precision beyond human capacity. AI has significantly impacted thyroid cancer diagnosis, offering advanced tools and methodologies that promise to revolutionize patient outcomes.
  • 109
  • 01 Nov 2023
Topic Review
Renal Cancer Management with AI and Digital Pathology
Renal cancer is a heterogeneous group of tumors with different histology, molecular characteristics, clinical outcomes and responses to treatment. The most common types are clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC).
  • 230
  • 31 Oct 2023
Topic Review
DNA Microarrays
Early disease detection using microarray data is vital for prompt and efficient treatment. However, the intricate nature of these data and the ongoing need for more precise interpretation techniques make it a persistently active research field. Numerous gene expression datasets are publicly available, containing microarray data that reflect the activation status of thousands of genes in patients who may have a specific disease. ThesGene expression microarrays, also known as DNA microarrays, are laboratory tools used to measure the expression levels of thousands of genes simultaneously, thus providing a snapshot of the cellular function (for technical details.e datasets encompass a vast number of genes, resulting in high-dimensional feature vectors that present significant challenges for human analysis. 
  • 244
  • 24 Oct 2023
Topic Review
Deep Learning-Based Data-Centric Approach for ASD Diagnosis
Autism spectrum disorder (ASD) is a neurological disorder that severely impairs the communication skills necessary for regular living. Most people with autism have mild difficulties but occasionally severe ones that necessitate specialized care. The accurate and early diagnosis of ASD is crucial for facilitating timely intervention and providing individualized care for affected individuals. Rapid advances in deep learning techniques have ushered in a new era of medical image analysis, particularly in the context of ASD detection using facial images.
  • 210
  • 22 Sep 2023
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