Prediction of Tuberculosis Disease Progression with AI Analysis of Clinical Data

Abstract

This study uses clinical data to create AI-driven prediction models for the evolution of tuberculosis (TB) illness utilizing an interpretivist approach as well as deductive methodology. The study illustrates the potential of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AI$</tex> in revolutionizing TB management by leveraging secondary data from various sources. The algorithms beat traditional diagnostic techniques in terms of TB progression prediction accuracy (87.5%) and sensitivity (88.2%). Analyses of subgroups demonstrate treatment plans that are customized for certain patient populations. Data privacy compliance and ethical issues are given top priority. Real-time monitoring and the integration of genetic data have been recommended as areas for future research. This study represents a major improvement in the prognosis of tuberculosis and illustrates the revolutionary potential of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{AI}$</tex> in clinical decision-making.

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