Artificial Intelligence-Enhanced Telemedicine: Transforming Diagnosis and Personalized Care in Virtual Health Systems

• Manasi Bendale

  Santa Clara University

  Author

The integration of artificial intelligence (AI) within telemedicine has revolutionized virtual healthcare delivery by enabling high-precision diagnostics and tailored treatment strategies remotely. This paper evaluates state-of-the-art AI models applied to medical imaging and clinical text data within telehealth platforms, highlighting significant improvements in diagnostic accuracy and clinical decision support. Using deep learning architectures like ResNet-50 for imaging and BERT for natural language processing of electronic health records, the framework demonstrates enhanced patient care personalization and operational efficiency. Survey feedback from clinicians and patients underscores AI's practical benefits in improving healthcare accessibility and quality in remote settings. Challenges, including algorithmic biases and data privacy risks, are discussed, along with future research directions aimed at sustaining AI-driven telemedicine enhancements to health systems worldwide.

1. Manasi Bendale, Santa Clara University

   I approach complex business and technology challenges through a machine learning and research-driven lens. I transform ambiguous problems into data-driven solutions by designing experiments, analyzing data, and developing scalable ML-driven approaches that support smarter decision-making.

   My background spans machine learning research, business analysis, product ownership, and software engineering, giving me an end-to-end perspective from problem discovery to model deployment. I work across the full lifecycle—framing research questions, exploring and modeling data, collaborating with engineering teams, and translating insights into intelligent products that solve real-world customer problems.

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