Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial.

BACKGROUND & AIMS: Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis. METHODS: We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. RESULTS: A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7-84.7) in the autonomous AI group and 72.1% (95% CI, 65.5-78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7-84.7) in the autonomous AI group and 75.5% (95% CI, 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9-96.1] vs 82.1% [95% CI, 76.5-87.7]; P = .016). CONCLUSIONS: Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790).

The systemized exploitation of temporary migrant agricultural workers in Canada: Exacerbation of health vulnerabilities during the COVID-19 pandemic and recommendations for the future.

In 2018, 55,734 jobs in Canadian agriculture were filled by temporary migrant workers, accounting for nearly 20 percent of total employment in this sector. Though referred to as temporary, those migrant workers often fill long-term positions and provide crucial support to the Canadian agricultural industry, which has seen an increasing disengagement from the domestic workforce in the last fifteen years. Health vulnerabilities faced by temporary migrant workers are already well documented. In addition, there are multiple systemic factors inherent within the structure and implementation of the Temporary Foreign Worker Program that contribute to the perpetuation of health inequities within this population. The COVID-19 pandemic has both exacerbated many of these disparities and further increased the risk of labour rights violations and vulnerability to exploitation for these workers. As Canada's 2020 growing season comes to an end, thousands of temporary migrant agricultural workers are returning to their native countries. With planning for next year's growing season already commencing, this timely analysis aims to examine health vulnerabilities faced by TMAWs during the COVID-19 pandemic. Five key areas are examined: occupational injuries, substandard living conditions, psychological difficulties, lack of access to healthcare and barriers in exercising labour rights. Building on this analysis, recommendations for policy and practice aimed at improving migrant workers' health are discussed.