Citations

Background: The rapid adoption of digital pathology and artificial intelligence (AI) in oncology creates multiple opportunities for precision diagnostics but also creates an urgent need for evidence-based standards to ensure safe and effective implementation. Goal: This article, developed by the Digital Pathology Association, presents recommendations for digital pathology as well as the validation and clinical utility of AI-enabled digital pathology tools in clinical practice. This guidance addresses analytical and clinical validation, algorithm reliability, and criteria for establishing clinical utility attributed to test use. Key recommendations emphasize separate validation of scanning processes and AI algorithms, concordance studies to support interscanner generalizability, rigorous assessment of accuracy and reliability in real-world settings, and clear description of algorithmic use limitations. These recommendations further provide frameworks for when AI may replace or augment existing diagnostic approaches, such as biomarker scoring, cancer diagnosis, and prognostic risk assessment. Conclusions: By considering payer, regulatory, and clinical perspectives, the recommendations promote transparency, trust, and reproducibility in digital pathology while encouraging value-based care delivery. We support responsible innovation in computational pathology, ensuring that AI applications achieve not only technical performance goals but also deliver measurable clinical benefit to patients.

PURPOSE Cancer survivors endure costly and time-intensive care throughout treatment and survivorship. Paid sick leave provides employment and financial stability while supporting time off for medical needs. We investigated whether paid sick leave is associated with financial hardship and overall and cancer-specific mortality risk in working adult cancer survivors. METHODS Working adults age 18-64 years with a cancer history were identified from the National Health Interview Survey (NHIS) between 2011 and 2023. Associations of paid sick leave and financial hardship (medical care delays, problems paying medical bills, worry about paying medical bills, and cost-related medication underuse) were examined with 2011-2018 and 2019-2023 data using separate multivariable logistic regressions because of survey redesign in 2019. Linked mortality files from the National Death Index were available for 2011-2018 respondents. The association of paid sick leave and mortality risk was examined with multivariable Cox proportional hazards models. RESULTS A total of 8,091 and 3,919 working cancer survivors were identified from the 2011-2018 and 2019-2023 NHIS, respectively. Paid sick leave was associated with decreased medical care delays because of cost (2011-2018 odds ratio [OR], 0.74 [95% CI, 0.60 to 0.93]; P = .008; 2019-2023 OR, 0.51 [95% CI, 0.35 to 0.74]; P < .001) but not consistently with other forms of financial hardship. In adjusted analyses, paid sick leave was associated with significant reductions in risk of overall mortality (hazard ratio [HR], 0.74 [95% CI, 0.61 to 0.9]; P = .003) and cancer mortality (HR, 0.74 [95% CI, 0.61 to 0.91]; P = .003). CONCLUSION In working cancer survivors, paid sick leave is associated with decreased care delays and lower overall and cancer-specific mortality risk, which may be due to maintained employment, household income, and health insurance coverage leading to improvements in treatment access, affordability, and overall survivorship care.