With the advances in artificial intelligence (AI), data‐driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision‐making by such algorithms is not trustworthy for clinicians and is considered a black‐box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer‐reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model‐agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree‐based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree‐based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI‐enabled health systems and medical devices and, ultimately, the quality of care and outcomes.

Background: Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods: This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results: The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion: The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

Background: Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. Objective: In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method. Methods: Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users’ choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups. Results: The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions. Conclusions: These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

Access to healthcare is critical for population health; however, geographic barriers persist especially in rural and deprived regions. This study aims to develop an overall composite potential spatial accessibility index to healthcare facilities and services in Sistan and Baluchestan Province in southeast Iran. This study employed the enhanced two-step floating catchment area (E2SFCA) method to create an overall composite spatial accessibility index for healthcare facilities and services in Sistan and Baluchestan Province, southeast Iran. Spatial accessibility for general practitioners, nursing, dentistry, midwifery, pharmacy, medical laboratory, nutrition, public health, radiology, psychology, environmental health, rural health workers, inpatient hospital beds, and five medical specialty services were calculated. Spatial accessibility scores were normalized from 0 to 1 (no access = 0, low = 0.01 to 0.33, moderate = 0.331 to 0.66, high = 0.661 to 1) and aggregated into overall indices of primary, secondary, and overall healthcare accessibility for each district. Inequality was assessed using the Lorenz curve and Gini coefficient analysis. Low geographic accessibility was found across Sistan and Baluchestan Province, especially in rural areas. Almost 75% of the population had low/no access to overall primary care services within a 30-minute drive time. For secondary care, nearly 45% had low/no access to hospital inpatient beds within a 30-minute drive time, and around 40% had low/no access to specialists within a 60-minute drive time. Just 11.6% of the population had high overall healthcare access. The calculated Gini coefficient of 0.517 for the overall spatial accessibility index to healthcare services in Sistan and Baluchestan Province highlights a highly unequal distribution of healthcare services. This study demonstrates a useful replicable methodology that combines individual service accessibility metrics into an overall spatial healthcare access index. Furthermore, this study provides evidence of major shortfalls in healthcare access across Sistan and Baluchestan Province. Targeted strategies are required to increase the availability and capacity of services in underserved communities. Improving geographic access is key for progressing towards universal coverage and better population health.