ARTIFICIAL INTELLIGENCE AND INTELLIGENT COMPUTING TECHNIQUES BASED TELEMEDICINE AND HEALTHCARE APPLICATIONS

Abstract

A new era in telemedicine and healthcare has emerged in recent years with the merging of Artificial Intelligence (AI) and Intelligent Computing Techniques (ICT). The creation of hybrid Deep Neural Networks (DNNs), which integrate the advantages of many AI and ICT methodologies to improve medical diagnosis, therapy, and patient care, has been a particularly noteworthy example of this change. This study sheds light on the transformative changes these technologies bring to the medical industry by providing an outline of the developments and applications of hybrid DNN in telemedicine and healthcare. Additionally, by offering customised healthcare solutions based on each patient's unique requirements and traits, these systems can enhance patient outcomes in general. The integration of telemedicine, which uses hybrid DNNs to enable remote patient consultations, monitoring, and diagnostics, is also explored in this work. In light of global health emergencies like the COVID-19 pandemic, where it is crucial to minimise physical contact, this has become even more important. With the assistance of AI and ICT, telemedicine makes sure that patients receive prompt, efficient care from the convenience of their homes, relieving the pressure on medical institutions. The ethical and legal issues surrounding the broad use of AI and hybrid DNN in healthcare are also covered in the article. The responsible implementation of these technologies requires careful consideration of critical issues such as patient privacy, data security, and regulatory compliance in the medical field. The integration of AI with ICT, particularly with the use of hybrid DNNs, has brought about a revolution in telemedicine and healthcare. Better patient outcomes and a more effective healthcare system are anticipated as a result of these technologies, which also offer remote healthcare services, personalised treatment programmes, and enhanced medical diagnostics. When compared to existing models, the proposed method demonstrates an impressive accuracy rate of 99%