Abstract

ARTIFICIAL INTELLIGENCE IN CLINICAL DATA MANAGEMENT: A REVIEW OF CURRENT APPLICATION AND FUTURE DIRECTIONS

Noorush Shifa Nizami*, Sameena Anjum, Anisetti Sai Manikanta and Shivani Vanamula

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Abstract

Artificial intelligence (AI) has been increasingly applied in clinical data management to improve data quality, accelerate data analysis, and facilitate decision-making in healthcare. This review paper aims to summarize the current applications of AI in clinical data management and discuss potential future directions. First, the review discusses the current use of AI in clinical data management, including natural language processing (NLP) for clinical documentation, machine learning (ML) for predictive analytics and risk stratification, and deep learning (DL) for image analysis. The review highlights the benefits of using AI in clinical data management, including improved efficiency, accuracy, and reproducibility of data analysis. Next, the review discusses potential future directions for AI in clinical data management, including the integration of AI with electronic health record (EHR) systems to facilitate real-time data analysis and decision-making, the use of AI to improve clinical trial design and patient recruitment, and the application of AI in precision medicine to tailor treatment plans based on individual patient data. The review also considers some of the challenges and limitations of using AI in clinical data management, including the need for high-quality data to train AI algorithms, the potential for bias in AI algorithms, and the need for human oversight to ensure the ethical use of AI in healthcare. In conclusion, AI has the potential to transform clinical data management by improving the accuracy, efficiency, and reproducibility of data analysis. However, ongoing research and development are needed to address the challenges and limitations of using AI in healthcare, and to ensure that AI is used in an ethical and responsible manner.

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