Generative artificial intelligence in oncology

Current Opinion in Urology, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

Purpose of review By leveraging models such as large language (LLMs) and generative computer vision tools, artificial intelligence (GAI) is reshaping cancer research oncologic practice from diagnosis to treatment follow-up. This timely provides a comprehensive overview the current applications future potential GAI in oncology, including urologic malignancies. Recent findings has demonstrated significant improving by integrating multimodal data, diagnostic workflows, assisting imaging interpretation. In treatment, shows promise aligning clinical decisions with guidelines, optimizing systemic therapy choices, aiding patient education. Posttreatment, include streamlining administrative tasks, follow-up care, monitoring adverse events. image analysis, data extraction, outcomes research. Future developments could stimulate discovery, improve efficiency, enhance patient-physician relationship. Summary Integration into oncology shown some ability accuracy, optimize decisions, ultimately strengthening Despite these advancements, inherent stochasticity GAI's performance necessitates human oversight, more specialized models, proper physician training, robust guidelines ensure its well tolerated effective integration practice.

Language: Английский

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Ontology-assisted GPT-based building performance simulation and assessment: Implementation of multizone airflow simulation

Energy and Buildings, Journal Year: 2024, Volume and Issue: 325, P. 114983 - 114983

Published: Nov. 2, 2024

Language: Английский

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Generative AI Models (2018–2024): Advancements and Applications in Kidney Care

BioMedInformatics, Journal Year: 2025, Volume and Issue: 5(2), P. 18 - 18

Published: April 3, 2025

Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), Vision (LVMs) in addressing critical challenges kidney care. GenAI supports research early interventions through generation synthetic medical data. LLMs enhance clinical decision-making by analyzing texts electronic records, while LVMs improve diagnostic accuracy advanced image analysis. Together, these technologies show promise for advancing patient education, risk stratification, diagnosis, personalized treatment strategies. highlights key advancements GenAI, LLMs, from 2018 2024, focusing on their applications care presenting common use cases. It also discusses limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, ethical concerns. By providing comprehensive analysis, this outlines roadmap integrating into nephrology, emphasizing need further real-world validation fully realize potential.

Language: Английский

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Large language models: Tools for new environmental decision-making

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124373 - 124373

Published: Feb. 1, 2025

Language: Английский

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Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes

Urology Practice, Journal Year: 2024, Volume and Issue: 11(5), P. 782 - 789

Published: Sept. 1, 2024

GPT-4 is a large language model with potential for multiple applications in urology. Our study sought to evaluate GPT-4's performance data extraction from renal surgery operative notes.

Language: Английский

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