Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Current Oncology, Journal Year: 2024, Volume and Issue: 31(9), P. 5255 - 5290

Published: Sept. 6, 2024

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning natural language processing, their applications HNC. The integration of with imaging techniques, genomics, electronic health records explored, emphasizing its role early detection, biomarker discovery, planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, real-time monitoring systems are poised to further advance field. Addressing these fostering among experts, clinicians, researchers crucial developing equitable effective applications. future HNC holds significant promise, offering potential breakthroughs diagnostics, personalized therapies, improved patient outcomes.

Language: Английский

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Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis

EClinicalMedicine, Journal Year: 2024, Volume and Issue: 71, P. 102555 - 102555

Published: March 22, 2024

Diagnosis is a cornerstone of medical practice. Worldwide, there increased demand for diagnostic services, exacerbating workforce shortages. Artificial intelligence (AI) technologies may improve efficiency, accuracy, and access. Understanding stakeholder perspectives key to informing implementation complex interventions. We systematically reviewed the literature on AI, including all English-language peer-reviewed primary qualitative or mixed-methods research.

Language: Английский

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Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts

Radiology Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6(4)

Published: July 1, 2024

The Radiological Society of North America (RSNA) and the Medical Image Computing Computer Assisted Intervention (MICCAI) have led a series joint panels seminars focused on present impact future directions artificial intelligence (AI) in radiology. These conversations collected viewpoints from multidisciplinary experts radiology, medical imaging, machine learning current clinical penetration AI technology radiology how it is impacted by trust, reproducibility, explainability, accountability. collective points-both practical philosophical-define cultural changes for radiologists scientists working together describe challenges ahead technologies to meet broad approval. This article presents perspectives MICCAI RSNA clinical, cultural, computational, regulatory considerations-coupled with recommended reading materials-essential adopt successfully and, more generally, practice. report emphasizes importance collaboration improve deployment, highlights need integrate imaging data, introduces strategies ensure smooth incentivized integration.

Language: Английский

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Effects of artificial intelligence implementation on efficiency in medical imaging—a systematic literature review and meta-analysis

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: Sept. 30, 2024

Language: Английский

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Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1)

Published: Jan. 1, 2024

Abstract Machine learning (ML) and deep (DL) have potential applications in medicine. This overview explores the of AI cardiovascular imaging, focusing on echocardiography, cardiac MRI (CMR), coronary CT angiography (CCTA), morphology function. AI, particularly DL approaches like convolutional neural networks, enhances standardization echocardiography. In CMR, undersampling techniques DL-based reconstruction methods, such as variational improve efficiency accuracy. ML CCTA aids diagnosing artery disease, assessing stenosis severity, analyzing plaque characteristics. Automatic segmentation structures vessels using is discussed, along with its congenital heart disease diagnosis 3D printing applications. Overall, integration imaging shows promise for enhancing diagnostic accuracy across modalities. The growing use Generative Adversarial Networks brings substantial advancements but raises ethical concerns. “black box” problem models poses challenges interpretability crucial clinical practice. Evaluation metrics ROC curves, image quality, relevance, diversity, quantitative performance assess GAI models. Automation bias highlights risk unquestioned reliance outputs, demanding careful implementation frameworks. Ethical considerations involve transparency, respect persons, beneficence, justice, necessitating standardized evaluation protocols. Health disparities emerge if training lacks impacting language models, GPT-4, face hallucination issues, posing legal healthcare. Regulatory frameworks governance are fair accountable AI. Ongoing research development vital to evolving ethics.

Language: Английский

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Versorgungsforschung zum Einsatz von künstlicher Intelligenz in der Gesundheitsversorgung

Springer eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 583 - 594

Published: Jan. 1, 2024

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