As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). This approach could also guide and control the implementation of AI systems in the healthcare industry. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The first level contained six key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. On the basis of the MAVT approach, three layers of attributes were created. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry.
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