Prediction of Tuberculosis Disease Progression with AI Analysis of Clinical Data
- Resource Type
- Conference
- Authors
- Pandey, Saroj Kumar; Singh, Kamred Udham; Dingankar, R. Shreyas; Jadhav, Kishor; Gupta, Kirti; Yadav, Ramesh Kumar
- Source
- 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), 2023 International Conference on. 1:1-6 Dec, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Geoscience
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Technological innovation
Sensitivity
Tuberculosis
Sociology
Predictive models
Data models
Real-time systems
AI-driven models
Disease progression
Clinical data analysis
Ethical considerations
- Language
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 $AI$ 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 $\text{AI}$ in clinical decision-making.