AI has the potential to revolutionize healthcare by enabling more accurate diagnoses, more effective treatment regimens, and improved patient outcomes. While AI is promising, many challenges remain including limited case studies from the real world, regulatory pressure, bias in data and integration into existing health care delivery systems. In this research we intend to overcome these challenges by designing a comprehensive framework to enhance transactive adoption of AI in healthcare. Cohorts combined with longitudinal case studies advance the study; ethical perspectives, data quality improvement, and bias mitigation emphasises justification for the validity and generalizability of the AI technologies used, which improves the quality of the study. Focus of the Research The research attempts to build interoperable AI systems (which can connect with current healthcare infrastrukture) by Ideating solutions for scalable AI Integration Additionally, it also discusses the challenges posed by hackers and criminal organisations, along with measures to promote patient data privacy, regulatory compliance, and the long-term effects of artificial intelligence on patient healthcare. Such understanding may facilitate an adequate implementation of AI by healthcare professionals and organizations as to impact patient safety, decrease costs and increase the outcome of patient population sorting for different clinical environments.
Rashid et al. (Tue,) studied this question.