Artificial intelligence and the impact of multiomics on the reporting of case reports

Abstract

The integration of artificial intelligence (AI) and multiomics has transformed clinical and life sciences, enabling precision medicine and redefining disease understanding. Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022, with AI research tripling during this period. Multiomics fields, including genomics and proteomics, also advanced, exemplified by the Human Proteome Project achieving a 90% complete blueprint by 2021. This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting. A review of studies and case reports was conducted to evaluate AI and multiomics integration. Key areas analyzed included diagnostic accuracy, predictive modeling, and personalized treatment approaches driven by AI tools. Case examples were studied to assess impacts on clinical decision-making. AI and multiomics enhanced data integration, predictive insights, and treatment personalization. Fields like radiomics, genomics, and proteomics improved diagnostics and guided therapy. For instance, the "AI radiomics, genomics, oncopathomics, and surgomics project" combined radiomics and genomics for surgical decision-making, enabling preoperative, intraoperative, and postoperative interventions. AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data. AI and multiomics enable standardized data analysis, dynamic updates, and predictive modeling in case reports. Traditional reports often lack objectivity, but AI enhances reproducibility and decision-making by processing large datasets. Challenges include data standardization, biases, and ethical concerns. Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine. AI and multiomics integration is revolutionizing clinical research and practice. Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential. Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.

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