โก Quick Summary
This study explores how artificial intelligence (AI) is transforming the diagnosis and treatment of chronic sinusitis (CRS), highlighting its ability to enhance imaging, pathology diagnosis, and prognostic predictions. The findings suggest that AI can significantly reduce turnaround times and diagnostic costs while improving patient outcomes.
๐ Key Details
- ๐ Research Scope: Focused on AI applications in CRS over the last 7 years.
- ๐งฉ Keywords: Artificial intelligence, machine learning, chronic sinusitis.
- โ๏ธ Methodology: Literature search across PubMed and Web of Science.
- ๐ Key Findings: AI can optimize diagnosis and treatment, but challenges remain.
๐ Key Takeaways
- ๐ค AI-assisted systems are emerging as valuable tools in CRS management.
- ๐ Turnaround times for diagnosis can be significantly reduced with AI.
- ๐ฐ Diagnostic costs are lowered through the use of AI technologies.
- ๐ AI enhances imaging and pathology diagnosis for more accurate assessments.
- ๐ Prognostic predictions are improved, leading to better treatment outcomes.
- โ ๏ธ Challenges include lack of standardization and data privacy concerns.
- ๐ Collaboration between healthcare providers is essential for AI integration.
- ๐ Recommendations for further research to maximize AI’s potential in CRS.
๐ Background
Chronic sinusitis (CRS) is a prevalent condition that poses significant challenges in terms of diagnosis and treatment. The need for standardized management is critical, as accurate diagnosis and individualized treatment plans are essential for effective patient care. The integration of artificial intelligence into medical practice offers promising solutions to these challenges, paving the way for enhanced clinical outcomes.
๐๏ธ Study
The study conducted a comprehensive review of literature from the past seven years, focusing on the application of AI in the diagnosis and treatment of CRS. By utilizing databases such as PubMed and Web of Science, the researchers categorized the findings based on clinical applications, including diagnosis, treatment, and prognosis prediction.
๐ Results
The results indicate that AI applications in CRS can lead to a significant reduction in turnaround times and lower diagnostic costs. Furthermore, AI has shown promise in enhancing the accuracy of imaging and pathology diagnoses, as well as improving prognostic predictions. However, the study also identified several challenges, including the need for standardized AI products and data privacy issues.
๐ Impact and Implications
The implications of this study are profound, as the integration of AI into CRS management could revolutionize how healthcare providers approach diagnosis and treatment. By leveraging AI technologies, clinicians can offer more personalized and efficient care, ultimately improving patient outcomes. The findings underscore the importance of continued research and collaboration to address existing challenges and fully realize the potential of AI in healthcare.
๐ฎ Conclusion
This study highlights the transformative potential of artificial intelligence in optimizing the diagnosis and treatment of chronic sinusitis. As AI technologies continue to evolve, they hold the promise of delivering more accurate, efficient, and personalized healthcare solutions. Ongoing research and collaboration will be crucial in overcoming the challenges identified and ensuring that AI can be effectively integrated into clinical practice.
๐ฌ Your comments
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Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis.
Abstract
BACKGROUND: Standardised management of chronic sinusitis (CRS) is a challenging but vital area of research. Not only is accurate diagnosis and individualised treatment plans required, but post-treatment chronic disease management is also indispensable. With the development of artificial intelligence (AI), more “AI + medical” application models are emerging. Many AI-assisted systems have been applied to the diagnosis and treatment of CRS, providing valuable solutions for clinical practice.
OBJECTIVE: This study summarises the research progress of various AI-assisted systems applied to the clinical diagnosis and treatment of CRS, focusing on their role in imaging and pathological diagnosis and prognostic prediction and treatment.
METHODS: We used PubMed, Web of Science, and other Internet search engines with “artificial intelligence”ใ”machine learning” and “chronic sinusitis” as the keywords to conduct a literature search for studies from the last 7 years. We included literature eligible for AI application to CRS diagnosis and treatment in our study, excluded literature outside this scope, and categorized it according to its clinical application to CRS diagnosis, treatment, and prognosis prediction. We provide an overview and summary of current advances in AI to optimize the diagnosis and treatment of CRS, as well as difficulties and challenges in promoting standardization of clinical diagnosis and treatment in this area.
RESULTS: Through applications in CRS imaging and pathology diagnosis, personalised medicine and prognosis prediction, AI can significantly reduce turnaround times, lower diagnostic costs and accurately predict disease outcomes. However, a number of challenges remain. These include a lack of AI product standards, standardised data, difficulties in collaboration between different healthcare providers, and the non-interpretability of AI systems. There may also be data privacy issues involved. Therefore, more research and improvements are needed to realise the full potential of AI in the diagnosis and treatment of CRS.
CONCLUSION: Our findings inform the clinical diagnosis and treatment of CRS and the development of AI-assisted clinical diagnosis and treatment systems. We provide recommendations for AI to drive standardisation of CRS diagnosis and treatment.
Author: [‘Liu YY’, ‘Jiang SP’, ‘Wang YB’]
Journal: Front Physiol
Citation: Liu YY, et al. Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis. Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis. 2025; 16:1522090. doi: 10.3389/fphys.2025.1522090