Prospects and Limits for Generative Artificial Intelligence for Medical Systems: Intelligent Healthcare Slides

The elderly population's increasing numbers, dwindling healthcare resources, and the perilous doctor-patient contact due to pathologies like COVID-19 signal a need for a shift in the current medical model. Yet, the question remains: what new model to adopt? Artificial Intelligence (AI), the foundation of contemporary life changes, pledges a transformative impact on patient care and the healthcare system. However, the genuine challenge lies in devising a practical model to curb the escalating demand for elderly operations amidst a larger populace with fewer health issues. The potential frontier emerges in Generative Artificial Intelligence, offering fresh data and models for future healthcare systems. These systems facilitate the interpretation of outcomes and the generation of personalized medicine. Leveraging digital twins, models can discern the treatment effects on patients through their digital counterparts. Additionally, AI wearable technology in virtual hospitals enables the remote monitoring of multiple patients within their homes. An overarching query persists: can these systems earn trust in medicine? Questions revolve around the accuracy, precision, sensitivity, ROC curve, and F-score of data produced by generative AI. Addressing these concerns, the latter part of this tutorial will contribute insights, utilizing metrics common in the world of Metrology