International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Revolutionizing Healthcare Diagnostics: Sophisticated AI methodologies for accuracy and accuracy in chronic disease forecasting Pandian Sundaramoorthy1, Rajesh Daruvuri 2, Balaram Puli3, N N Jose4, RVS Praveen5, Senthilnathan Chidambaranathan6 1Application Developer, EL CIC-1W-AMI, IBM, , 6303 Barfield Rd NE Sandy Springs, GA, 30328 USA
2 Independent Researcher , Cloud, Data and AI, University of the Cumbarlands , USA, GA , Kentucky 3Senior SRE and AI/Big Data Specialist, Engineering and Data Science, Everest Computers Inc. 875 Old Roswell
Road Suite, E-400, Roswell, GA 30076, USA Consultant/Architect, Denken Solutions, California, USA 5 Director, Product Engineering, LTIMindtree, USA, 6 Associate Director / Senior Systems Architect, Architecture and Design. Virtusa Corporation, New Jersey, USA ----------------------------------------------------------------------***--------------------------------------------------------------------4
Abstract - Computing methods for medical diagnoses have
seems to have been impacted massively. [1] The incorporation of AI techniques in diagnostic practice has replaced manual or conventional models with AI-based intelligent models that can deliver accuracy and scalability updated in the modern world, using computers.[2] Hyped chronic diseases including cardiovascular, neurological, hepatic, renal and prostate conditions comprise a large fraction of the entire disease spectrum[3] hence, need to find strategies for early detection and diagnostic accuracy through innovative approaches. Most diagnostic methods in the traditional approach involve quite a lot of human interference, which results in errors, delayed outputs or even varying results. Such limitations are even worsened by increased volume and variability of medical data and the increasing rate of development of chronic diseases.[4] Molecular computation, applying complex electronic systems and advanced computations, has become an innovative solution which combats these problems quite effectively. [5] Fig.1 The purpose of this study is to identify the development of AI in diagnosing systems of the healthcare sector in the last decade and focus on the role of AI approaches in the early diagnosis of chronic diseases. [6] In this cross-sectional study, the author analysed 105 relevant articles from the databases and shed light on the accomplishments to date, the current issues, and future developments of AI in healthcare.
been enhanced through AI techniques in 2024, as well as being precise, swift and able to scale up in a way that previous methods cannot. Today’s AI systems provide diagnostic accuracy rates of over 95% better than traditional methods by avoiding false positives/negatives. It is seen that Deep Learning models, including Convolutional and Recurrent Neural Networks are very useful for image diagnostics and Structured Clinical Data analysis, whereas ML algorithms show an extraordinary performance. It has founded for disease diagnosis in the initial stage, patient’s risk assessment, and treatment planning, which are the major shortage areas in healthcare. Nonetheless there are limitations including imbalanced data, the sophistication of AI algorithms, and ethical issues of patients’ privacy. To address these problems, the study’s proposed solutions include federated learning for secure data sharing, humaninterpretable AI for decision-making with sealed data and a combination of multiple methodologies, called hybrid AI. Using such next-generation methods, AI can be precise and accurate to about 98% on complex cases, much of which are reliable. The study also provides a paradigm shift in the future perspective of AI where AI must work hand in hand with real-time data analytics, wearable systems, and even cloud systems, and make them a dynamic diagnostic system. It would deepen the role and use of health care delivery system across the world, redeploy diagnostics to be accurate, appreciative and personalized to patients’ needs. Key Words: Machine intelligence, diagnosis precision, reinforcement, learning, chronic, condition prognosis, decentralized training, explainable artificial intelligence, health evolution, prognosis.
Fig.1. Block diagram of the diagnosis process.
1.INTRODUCTION
It also explores limitations to the practice of AI, including data skewness, high-order model interpretability, and the ethical consideration of patient’s privacy. From such analysis, it highlights state of the art techniques such as
Today AI is progressing at such a high rate that many fields have been transformed; however, the realm of healthcare
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 151