Impact of Machine Intelligence on Clinical Disease Outbreak Prediction
algorithmic bias; artificial intelligence; disease outbreak prediction; public health
This research paper examines the utilization of Artificial Intelligence (AI) in disease outbreak prediction and its importance in public health. It explores the hurdles associated with predicting disease outbreaks, including data quality and accessibility, ethical considerations, algorithmic bias, and integration and interpretability challenges. The paper presents an overview of AI techniques applied in healthcare and their relevance to forecasting disease outbreaks. Case studies demonstrate the efficacy of AI -based models in predicting infectious diseases, vector-borne diseases, and epidemics/pandemics, employing diverse data sources. The limitations and future prospects of AI in disease outbreak prediction are addressed, accompanied by recommendations for enhancement. In conclusion, the paper highlights AI's potential to revolutionize disease outbreak prediction, leading to proactive public health interventions and improved response strategies. 2023 IEEE.
Singhal M.; Mishra S.; Sharma V.; Anand K.; Alkhayyat A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 1020-1025.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/UPCON59197.2023.10434492 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187352329&doi=10.1109%2fUPCON59197.2023.10434492&partnerID=40&md5=6f6d4d2fb4eedfe0a8f8ecdd463e04b6
Singhal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Anand K., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq