Advancing Medical AI: Literature Review of Intelligent Assistants in Healthcare Queries

The advanced instrument of AI tools is being utilized in the healthcare industry including nat- ural language processing (NLP), and machine learning (ML), which have backing from the following document frameworks: These frame- works offer a rational approach to reasoning about using and experiencing small scale AI in- novations such as voice control and Media En- tity/LLM at workplaces, including clinical set- tings. New advancement in the field of artificial intelligence is changing the face of healthcare enabling the creation of VHAs that are unin- terrupted, efficient and logged on. VHAs pro- vide help with the identification of the symp- toms’ signs, aiding the patient in a variety of decisions including those that involve medical, school nutrition, and general health decisions. With the implementation of semantic interfaces, VHAs mimic ‘subjective’ communication with the doctors, and the nurses, enhancing the ex- perience of noting down health issues, and pro- moting patient-orientated care. These tools are mainly useful when applied to chronic diseases, in order to decrease clinical personnel loads, and when patient participation is desired and en- couraged, including patient education. Some LHMs including GPT-4 improve the re- alism and clinical utility of VHAs since they per- form clinical inquiries accurately and coherently. Nevertheless, such confidence has been built on content reliability, service quality, and Copy and Data Protection tendencies that reinforce the necessity of the clear ethical rules and norms. As VHAs assume more roles in clinical mat- ters and routine operations, goal accomplish- ment would necessitate interdisciplinary collab- oration to achieve VHAs integration that corre- sponds to established professions’ norms besides enhancing accountable healthcare delivery that patient-consumers expect.