A Review on Explainable Artificial Intelligence in Healthcare BPOS-Application and Challenges for Sustainability of Business

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) across healthcare domains has significantly improved diagnostic accuracy, operational efficiency, and patient outcomes (Rajkomar et al., 2019; Jiang et al., 2017). Despite these benefits, concerns regarding the lack of transparency and interpretability in AI systems remain critical, particularly in high-stakes environments such as healthcare (Doshi-Velez & Kim, 2017; Tjoa & Guan, 2020). Many AI models operate as “black boxes,†making it difficult for stakeholders to understand the rationale behind their decisions, thereby raising issues of trust, accountability, and ethical compliance (Arrieta et al., 2020; Floridi et al., 2018). Healthcare Business Process Outsourcing (BPO) organizations increasingly utilize AI-driven tools to automate medical document processing for insurance claims. These documents, including prescriptions, lab reports, and radiology records, must be classified and chronologically organized to reflect patient history. Traditional ML approaches such as Count Vectorization and TF-IDF combined with classification algorithms achieve high accuracy; however, they rely heavily on word frequency, which can introduce bias and lead to misclassification (Wang et al., 2018; Obermeyer et al., 2019). To address these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising solution that enhances transparency and interpretability in AI systems (Ribeiro et al., 2016; Lundberg & Lee, 2017). This study presents a systematic review of XAI techniques in healthcare, focusing on their application in medical document classification. It further identifies key challenges, including the lack of standardized evaluation metrics, and proposes future research directions to enhance transparency, fairness, and reliability in AI-driven healthcare systems (Arrieta et al., 2020; Tjoa & Guan, 2020).