A Comprehensive Study on AI and ML Techniques in Healthcare Diagnostics Challenges and Future Directions

Abstract

Artificial intelligence is a field of computer science which focuses on creating machines which performs task based on human intelligence. The process consists of learning from which information can be gathered, reasoning is done to reach the output by processing rules finally correction is done for future processing. On other hand the concept of robotics involves phases such as designing, building and performing operation. Despite the advancements there is a critical research gap in understanding the challenges of AI adoption in healthcare diagnostics particularly with regard to infrastructure limitations, cost considerations, ethical issues, and the disparity in acceptance between urban and rural areas. This disparity also includes the scant investigation of AI models tailored to the provision of individualized equal healthcare.  Designing phases involves building a blue print. Construction is used for Incorporating AI components according to the needs of user. Operations involves working of a robot similar to that of human in performing tasks which produces more number of efficiency.  The main ideology of incorporating AI and robotics in healthcare is to offer enhanced outcomes with better accuracy rate and efficiency for health care ecosystem. The review article gives you an overview about advanced technologies of AI and robotics applications and mechanism for medical diagnosis. The latest algorithms can be used in field of diagnosis and personalised medical treatment In this article cancer data set has been trained using ML model and comparison has been made identifying best performing model. To obtain this the data set should be pre-processed at the first stage. After the first stage future extraction has been performed and finally classification algorithms for machine learning have been added. The topic of context has been extracted using data set. Support vector machine, naïve bayes and entropy has been utilised for context extraction. Key findings demonstrate that Support Vector Machines provide a reliable method for AI-driven diagnostics by outperforming previous models in terms of accuracy, recall, and precision time. This study also suggests practical approaches to get beyond adoption hurdles, such as cost-cutting measures, ethical frameworks, and scalable infrastructure solutions.