Positive developments from regulatory bodies, biopharma and software developers followed by continued investments such as the acquisition of Inspirata by Fujifilm, have framed digital pathology as a rapidly growing market with true translational and clinical benefits. However, for this to become a standardized, cost-effective and widely implemented technology in biopharma, critical bottlenecks in preclinical and translation research need to be overcome.

As such, the inaugural Translational Digital Pathology & AI Summit arrived as a pioneering platform singularly focused on uniting and empowering drug developers to make more informed treatment decisions using digital pathology and AI in pre-clinical and translational research to develop safer and more effective drugs and make informed decisions to diagnose and treat patients more accurately. Here are some of the top reasons you should have attended:

	Increase lab workflow and diagnostic accuracy by paving the implementation & regulatory approval path for digital pathologies with insights from GSK & AstraZeneca Standardize digital pathology protocols and best practices for seamless integration and transformative translational benefits with input from Mayo Clinic & Novartis
	Harness AI and spatial biology to enhance biomarker discovery and disease monitoring to expand treatment options for patients with insight from Sanofi & Rapare Therapeutics
	Accelerate biomarker and endpoint validation and patient stratification with multiplexing, multi-modeling and multi-omics to increase disease diagnosis with input from Gilead and Genmab
	Pave the path to better pathological data handling to improve image quality whilst minimizing cost and variabilities towards accurate and effective translational and clinical findings with insight from Moderna & Pfizer

Whether new or established in your digital pathology and AI journey, you could have joined your community to align with regulatory frameworks and ensure practical applications for biomarker discovery, endpoint validation and patient stratification to improve accuracy and precision of diagnosis and treatment options for patients in need.