Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians’ treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

Inter simple sequence repeat markers were employed for the genotyping of 16 plantain ecotypes. Two different electrophoretic systems namely conventional gel electrophoresis (CVGE) and fully automated high‐resolution CGE were used to evaluate the genetic diversity. Comparative analysis indicated that all parameters related to marker informativeness were higher in CGE except polymorphic information content. But genetic diversity parameters like effective number of alleles, Nei's gene diversity (1973) and Shannon's information index showed higher values (1.52 ± 0.12, 0.34 ± 0.05 and 0.52 ± 0.05, respectively) in CVGE as against CGE (1.29 ± 0.04, 0.22 ± 0.02 and 0.38 ± 0.03, respectively) system. The unweighed pair group method with arithmetic averages was used to obtain the dendrogram for both analyses. The results of dendrogram and principal component analysis were found to be consistent in both systems except for some minor disagreements. The clone‐specific bands could be used in the identification and development of SCAR markers. Inter simple sequence repeat markers used in this study provided sufficient polymorphism and reproducible banding pattern for evaluating the genetic diversity of different plantain ecotypes. Lack of accuracy and consistency of the CVGE warrants the employment of high‐throughput CGE for diversity analysis as it provided better separation of bands with higher resolution.

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