Systems that are stable and at the same time effective in providing Health Care to everyone, can be developed through biomedical intelligence. The article examines how Machine Learning (ML) and Artificial Intelligence (AI) have changed: accuracy of diagnosing, developing new treatment options, monitoring patients in real-time, and providing sustainable Health Systems. AI- based technologies (CNNs, GANs, RNNs, and Transformers) have shown us high levels of accuracy for diagnosis (up to 99.5%) and have reduced time to develop new drugs by 40%. By combining all types of data (EHRs, wearable devices, and genomic platforms) in an integrated manner, we provide care that is personalized in advance and proactively (examples to include: early identification of patients with sepsis; understanding of patients with arrhythmias, and optimally managing diabetes). This study provides a detailed overview of how an AI-driven The Biomedical Innovation Framework (AIBF), which integrates sustainability principles, clinical deployment, computational intelligence, and data interoperability, can reduce energy usage by up to 30% and enhance resource utilization by 22%. The main facilitators of responsible and trustworthy adoption are considered to be explainable AI, fairness, ethical governance, and privacy-preserving strategies like federated learning. Through workable technical and policy- driven solutions, issues with data quality, model generalization, infrastructure expenses, and regulatory ambiguity are resolved. In explainability, edge computing, global health equality, and sustainable AI governance, future research opportunities are described. Overall, the intersection of biomedical intelligence and sustainable health management shows a revolutionary way to achieve precision therapy, preventive care, accurate diagnosis, ethical AI deployment, and alignment with global health priorities, such as the Sustainable Development Goals of the UN.
Karve et al. (Fri,) studied this question.