Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence

Clinical Medicine Insights Oncology, Journal Year: 2025, Volume and Issue: 19

Published: Jan. 1, 2025

Despite the expanding therapeutic options available to cancer patients, resistance, disease recurrence, and metastasis persist as hallmark challenges in treatment of cancer. The rise prominence generative artificial intelligence (GenAI) many realms human activities is compelling consideration its capabilities a potential lever advance development effective treatments. This article presents hypothetical case study on application pre-trained transformers (GPTs) metastatic prostate (mPC). explores design GPT-supported adaptive intermittent therapy for mPC. Testosterone prostate-specific antigen (PSA) are assumed be repeatedly monitored while may involve combination androgen deprivation (ADT), receptor-signalling inhibitors (ARSI), chemotherapy, radiotherapy. analysis covers various questions relevant configuration, training, inferencing GPTs mPC with particular attention risk mitigation regarding hallucination problem implications clinical integration GenAI technologies. provides elements an actionable pathway realization GenAI-assisted As such, expected help facilitate trials GenAI-supported

Language: Английский

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Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective

Molecular Cancer, Journal Year: 2024, Volume and Issue: 23(1)

Published: Sept. 18, 2024

Language: Английский

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Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic

BMC Infectious Diseases, Journal Year: 2024, Volume and Issue: 24(1)

Published: Aug. 8, 2024

Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy information in multilingual contexts. This study aimed compare AI model efficiency English Arabic for infectious disease queries.

Language: Английский

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Sağlık Hizmetlerinde Yapay Zeka: Temel Kavramlar ve Sınıflandırmalar

Published: Jan. 7, 2025

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Evaluation of the Accuracy of Artificial Intelligence (AI) Models in Dermatological Diagnosis and Comparison With Dermatology Specialists

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Recent advances in generative artificial intelligence (AI) have expanded its applications diagnostic support within dermatology, but clinical accuracy requires ongoing evaluation. This study compared the performance of three advanced AI models, ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, with that board-certified dermatologists, using a dataset 30 cases encompassing variety dermatological conditions. The models demonstrated comparable to, sometimes exceeding, specialists, particularly rare complex cases. Statistical analysis revealed no significant difference rates between indicating may serve as valuable supplementary tool practice. Limitations include small sample size potential selection bias. However, these findings underscore progress AI's capabilities, supporting further validation larger datasets diverse scenarios to confirm practical utility.

Language: Английский

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Bayesian approaches for revealing complex neural network dynamics in Parkinson’s disease

Journal of Computational Science, Journal Year: 2025, Volume and Issue: unknown, P. 102525 - 102525

Published: Jan. 1, 2025

Language: Английский

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Multimodal Metaverse Healthcare: A Collaborative Representation and Adaptive Fusion Approach for Generative AI-Driven Diagnosis

Research, Journal Year: 2025, Volume and Issue: 8

Published: Jan. 1, 2025

The metaverse enables immersive virtual healthcare environments, presenting opportunities for enhanced care delivery. A key challenge lies in effectively combining multimodal data and generative artificial intelligence abilities within metaverse-based applications, which is a problem that needs to be addressed. This paper proposes novel learning framework healthcare, MMLMH, based on collaborative intra- intersample representation adaptive fusion. Our introduces approach captures shared modality-specific features across text, audio, visual health data. By encoders with carefully formulated intrasample collaboration mechanisms, MMLMH achieves superior feature complex assessments. framework’s fusion approach, utilizing attention mechanisms gated neural networks, demonstrates robust performance varying noise levels quality conditions. Experiments datasets demonstrate MMLMH’s over baseline methods multiple evaluation metrics. Longitudinal studies visualization further illustrate adaptability evolving environments balanced diagnostic accuracy, patient–system interaction efficacy, integration complexity. proposed has unique advantage similar level of maintained various patient populations avatars, could lead greater personalization experiences the metaverse. successful functioning such complicated circumstances suggests it can combine process information streams from several sources. They successfully utilized next-generation delivery through reality.

Language: Английский

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Foundations and Emerging Trends in Generative Artificial Intelligence (AI) for Industrial Applications

Published: Jan. 1, 2025

Language: Английский

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AI for image quality and patient safety in CT and MRI

European Radiology Experimental, Journal Year: 2025, Volume and Issue: 9(1)

Published: Feb. 23, 2025

Language: Английский

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Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine

FEBS Open Bio, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes individuals to enable personalized medicine. With increasing complexity omics data, particularly multiomics, there is a growing need for advanced computational frameworks interpret these data effectively. Foundation models (FMs), large‐scale machine learning pretrained on diverse types, have recently emerged as powerful tools improving interpretability decision‐making in precision This review discusses integration FMs into MDT systems, their role enhancing multiomics data. We examine current challenges, recent advancements, future opportunities leveraging analysis MDTs, with focus application

Language: Английский

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