Artificial intelligence-assisted nursing interventions in psychiatry for oral cancer patients: A concise narrative review

Antibiotic resistance (ABR) is a major global health threat that puts decades of medical progress at risk. Bacteria develop resistance through various means, including modifying their targets, deactivating drugs, and utilizing efflux pump systems. The main driving forces behind ABR are excessive antibiotic use in healthcare and agriculture, environmental contamination, and gaps in the drug development process. The use of advanced detection technologies, such as next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics, and metagenomics, has greatly improved the identification of resistant pathogens. The consequences of ABR on public health are significant, increased mortality rates, the endangerment of modern medical procedures, and resulting in higher healthcare expenses. It has been expected that ABR could potentially drive up to 24 million individuals into extreme poverty by 2030. Mitigation strategies focus on antibiotic stewardship, regulatory measures, research incentives, and raising public awareness. Furthermore, future research directions involve exploring the potential of CRISPR-Cas9 (CRISPR-associated protein 9), nanotechnology, and big data analytics as new antibiotic solutions. This review explores antibiotic resistance, including mechanisms, recent trends, drivers, and technological advancements in detection. It also evaluates the implications for public health and presents strategies for mitigating resistance. The review emphasizes the significance of future directions and research needs, stressing the necessity for sustained and collaborative efforts to tackle this issue.

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

Pham,

Teh,

Chatzopoulou

et al. 2024

Current Oncology

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

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Artificial intelligence-assisted nursing interventions in psychiatry for oral cancer patients: A concise narrative review

Cited by 9 publications

References 56 publications

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Recent developments in antibiotic resistance: an increasing threat to public health

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

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