Citation:

Mohit Surender Reddy, Manikanth Sarisa, Siddharth Konkimalla, Sanjay Ramdas Bauskar, Hemanth Kumar Gollangi, Eswar Prasad Galla, Shravan Kumar Rajaram, 2021. "Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting", ESP Journal of Engineering & Technology Advancements, 1(2): 188-200.

Abstract:

The most common of these technologies include artificial intelligence (AI) as well as machine learning (ML), both of which are revolutionizing the healthcare system, especially in disease prediction. Given the emerging data produced from systems in healthcare, EHRs, social media, environment, and genomics, AI and ML algorithms continue to find genuine applications in anticipating disease outbreaks or patients’ health futures. Disease forecasting is looked at in this paper with regard to the different methods and algorithms being used in current practice, as well as the possibility that AI/ML could do more in identifying patterns and relationships that are difficult to decipher using traditional statistical analytical tools. It also presents issues that are associated with the adoption of AI/ML in medicine for instance, data protection, prejudice in the algorithms, and the intractability of the AI/ML models. There is potential seen in the use of AI in disease prediction in that it will help in the early detection of disease outbreaks, modelling of chronic disease and progression, and development of treatment plans. These developments have already been implemented in healthcare organizations globally and have positively impacted patient satisfaction as well as the management of healthcare. However, with such prospects come steep obstacles, as there are technical and ethical constraints that need to be crossed before the full potential of AI/ML in disease prediction can be achieved. This article has the purpose of reviewing the current AI/ML developments in disease forecasting, showing how they are used in the field including epidemiology, oncology, cardiovascular diseases and rare diseases. Finally, it highlights some trends, both synergistic and adverse, the concept of ethical decision-making and other aspects of this fairly new and dynamic discipline.

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Keywords:

Artificial Intelligence, Machine Learning, Disease Forecasting, Predictive Analytics, Epidemiology, Algorithmic Bias.