Dr.AssistAI: A Comprehensive Survey on AI-Based Medical Counseling and Multi-Disease Prediction Systems

Ever since the introduction of AI and ML in healthcare, intelligent systems have been designed both for medical counseling and for prediction of various diseases.In this paper, a detailed survey of AI-based techniques within the "Dr.AssistAI" framework is presented, emphasizing the automation of disease diagnosis, risk appraisal, and personalized patient counseling.Different methods, including CNNs, ensemble methods, transformer-based NLP models, and federated learning, are examined for their contributions toward predictive accuracy, interpretability, and privacy of data.The various healthcare datasets considered in this study, such as MIMIC-III and UK Biobank, present a case for the advances in multimodal data integration and real-time monitoring by wearable devices.Key challenges have also been identified, including data heterogeneity, model transparency, and ethical considerations for large-scale clinical deployment.The conclusion of the survey puts across the view that an AI-based system has enormous prospects to enhance traditional healthcare delivery via early diagnosis, continuous patient engagement, and preventive care strategies, eventually contributing to clinical efficiency and improved patient outcomes.