Explainable AI Based Interpretation With Big Data Analytics for Healthcare Industry—A Review

Big data analytics plays a crucial role in providing usable insights for improving the strategic planning of the diagnosis methods in the healthcare industry, and here the big data helps the clinical professionals analyze the large numbers of datasets obtained from different demographic groups to identify which factor is blocking the patients from taking up their treatment. Despite the benefits guided by the information age, healthcare technologies encounter difficulties such as data protection, loss, and management issues. Artificial intelligence (AI) has helped more in the adaptation and handled the pandemic situations. However, the realization of AI's potential in healthcare is tempered by concerns regarding the trustworthiness and explaining ability of AI systems. ML algorithms have shown promise in detecting and managing healthcare complications, while the deployment of critical medical Internet of Things applications necessitates efficient mechanisms to address patient privacy concerns. Furthermore, the use of AI and machine learning (ML) in healthcare enables disease outbreak prediction, highlighting the versatility of AI in the health sector. However, there are still obstacles to achieving interpretability and accuracy in AI systems. Given these challenges, explainable AI (XAI) methods that are technically capable, ethically responsible, and meet the needs of healthcare practitioners are increasingly important. This review sky‐scrapes the importance of transparency, effective data management, human oversight, and international collaboration in advancing XAI in healthcare. Ultimately, the paper calls for the generalization of XAI frameworks and the prioritization of interpretation, data privacy, and security for future research, which has the full potential of XAI in revolutionizing healthcare.