Big data and the rise of AI-driven pathology

• Author Details:   
• Indranil Chakrabarti ^*
• Sujaya Mazumder

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Diagnostic pathology has significantly evolved beyond its traditional reliance on optical microscopy. The integration of Big Data, artificial intelligence (AI), and digital tools is transforming the field by enabling comprehensive analysis of digital images, genomic information, and clinical data simultaneously. This advancement is particularly impactful in oncology, where pathomics—the intersection of image analysis and data science—extracts detailed molecular and clinical patterns from extensive collections of high-resolution slides and multi-omic datasets. AI has transitioned from a conceptual term to a practical instrument capable of automating complex tasks such as tumor grading and mutation prediction, which improves diagnostic accuracy and supports tailored therapeutic strategies. The infrastructure of whole-slide imaging coupled with cloud storage facilitates efficient data management and global collaboration. However, challenges remain regarding platform interoperability, data security, and clinical validation of AI workflows. Ethical considerations require addressing biases in AI training datasets, safeguarding patient privacy, and ensuring equitable access to these technologies across diverse healthcare settings. Big Data analytics enhance clinicians' ability to predict tumor behavior and treatment response by integrating various biological datasets. Consequently, the role of the pathologist has expanded to encompass combining clinical expertise with data science, interpreting complex information to inform patient management. This collaboration between expert human judgment and advanced computational methods marks a shift from descriptive pathology toward predictive diagnostics. The ongoing challenge lies in managing the increasing volume of data while maintaining a focus on improving disease understanding and patient outcomes.