International Journal of Science, Engineering and TechnologyAuthors: Madhuri Zawar, Sanmati Kumar Jain, Pradnya Vikhar
Abstract: Telemedicine is now an essential care delivery mode, but patients (especially in rural and resource-restricted environments) continue to experience challenges associated with access, symptom interpretation, and communication with providers. The development of Artificial Intelligence (AI), Natural Language Processing (NLP) and Generative Adversarial Networks (GANs) presents the opportunities to address these shortcomings. This review summarizes the latest papers on AI-enables telemedicine with the focus on: (i) telehealth applications augmented by AI analytics, (ii) NLP models of clinical records and chatbots, and (iii) GAN-based models of synthetic medical data. Recent literature was located by searching large digital libraries on the keyword’s telemedicine, NLP, GANs, and patient-provider communication and filtered on remote-care applications. The reviewed articles present a positive change in predicting risks, remote monitoring, imaging, and dialogue support, whereas patient-centered communication outcomes (e.g., comprehension, satisfaction, trust) are mentioned less frequently, and most studies use accuracy-oriented indicators, which are not statistically validated at the time. Judging by these gaps, we propose the concept of MedVersa as a conceptual framework, which includes GAN-based data generation with large language model (LLM) dialogue features to increase the dependability, fairness, and contextual sensitivity of teleconsultations and guide the implementation and evaluations in the future. This article contains a literature review and conceptual architecture, and does not indicate implemented prototype or new empirical findings.
DOI: http://doi.org/10.5281/zenodo.20741370
