Background: Healthcare information systems are hindered by delayed data sharing, privacy breaches, and lack of patient control over data. The growing need for secure, privacy-preserved access control interoperable in health informatics technology (HIT) systems appeals to solutions such as Blockchain (BC), which offers a decentralized, transparent, and immutable ledger architecture. However, its current adoption remains limited to conceptual or proofs-of-concept (PoCs), often relying on simulated datasets rather than validated real-world data or scenarios, necessitating further research into its pragmatic applications and their benchmarking. Objective: This systematic literature review (SLR) aims to analyze BC-based healthcare implementations by benchmarking peer-reviewed studies and turning PoCs or production insights into real-world applications and their evaluation metrics. Unlike prior SLRs focusing on proposed or conceptual models, simulations, or limited-scale deployments, this review focuses on validating practical BC real-world applications in healthcare settings beyond conceptual studies and PoCs. Methods: Adhering to PRISMA-2020 guidelines, we systematically searched five major databases (Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect) for high-precision relevant studies using MeSH terms related to BC in healthcare. The designed review protocol was registered with OSF, ensuring transparency in the review process, including study screening by independent reviewers, eligibility, quality assessment, and data extraction and synthesis. Results: In total, 82 original studies fully met the eligibility criteria and narratively reported BC-based healthcare implementations with validated evaluation outcomes. These studies highlight the current challenges addressed by BC in healthcare settings, providing both qualitative and quantitative data synthesis on its effectiveness. Conclusions: BC-based healthcare implementations show both qualitative and quantitative effectiveness, with advancements in areas such as drug traceability (up to 100%) and fraud prevention (95% reduction). We also discussed the recent challenges of focusing more attention in this area, along with a discussion on the mythological consideration of our own work. Our future research should focus on addressing scalability, privacy-preservation, security, integration, and ethical frameworks for widespread BC adoption for data-driven healthcare.

AI-driven decision support systems possess the capability to improve health outcomes. However, advanced AI-driven systems face challenges related to interpretability and slow real-world adoption due to the use of abstract, black-box models. As a result, Explainable AI (XAI) approaches are evolving to provide explanations for predictions. Nonetheless, the predominant XAI approaches have several significant drawbacks. Firstly, these techniques require an understanding of model internals; secondly, they lack the potential to provide insights into feature interaction; and thirdly, they cannot generate alternative solutions based on What-if scenarios. In this context, counterfactual explanations (CEs) can improve the explainability of black-box models by generating what-if scenarios for input data points. However, producing clinically plausible CEs is complex due to the presence of the "Rashomon Effect", multiple conflicting objectives, and the trade-offs among them. This requires a robust Mechanism that ensures the interpretability and plausibility of CFs under balanced, optimized constraints. To address the above issues, this study proposes a novel multiobjective optimized counterfactual explanation (MoCE) methodology for generating clinically significant CEs. Our proposed methodology consists of two phases. Firstly, a Random Forest (RF) classifier was trained on the chronic kidney disease (CKD) dataset, using a single instance from the dataset as a patient profile to obtain the predicted outcome. Secondly, for baselines, we generated five CEs using diverse counterfactual explanations (DiCE) using a CKD instance. Next, we applied the NSGA-II algorithm-based MoCE methodology to generate multiple CEs against the same CKD instance. We compared both approaches, DiCE and the MoCE, qualitatively and quantitatively, for generating clinically plausible CEs while efficiently handling the confidence, validity, and sparsity trade-offs. Clinical relevance and significance of the CEs were assessed in an initial clinical evaluation conducted by a nephrologist expert. Both approaches achieve 100% validity. DiCE achieved a mean sparsity = 0.175 by changing approximately 2 features with medium confidence levels (0.59). Conversely, the proposed method generated CEs with enhanced confidence levels (0.9) by altering 7 attributes with sparsity (0.8-0.9), yielding more diverse and plausible solutions. The proposed MoCE methodology generated 100 Pareto-optimal CEs candidates, indicating a rich trade-off surface among validity, proximity, prediction confidence, and sparsity. The comparison highlights that DiCE generates CEs by altering a minimum number of features, resulting in highly sparse but less diverse and less clinically relevant CEs. In contrast, the proposed MoCE approach generated a diverse set of CEs by simultaneously optimizing multiple objectives, including distance, sparsity, and prediction confidence. This demonstrates a balanced handling of trade-offs, yielding more plausible CEs, particularly in high stake domains where feature interactions are crucial. This also suggests that the proposed MoCE provides a balanced and clinically meaningful alternative to existing methods.

Early and accurate diagnosis of critical diseases is essential for timely intervention and improved patient outcomes. While state-of-the-art machine learni