In a major market-positioning endeavour, Silicon valley-based start-up iNDX Technology has rechristened itself as “iNDX.Ai” – highlighting the importance of artificial intelligence (AI) which is the predominant technology driving their flagship informatics platform iCore V4.0TM. Artificial intelligence, like the blockchain technology and personalized medicine, has become the next poster child of tech pundits – who are betting heavily on the pivotal and disruptive role of this technology in healthcare. According to a recent report by Accenture’s, AI-inspired clinical technologies will enjoy tremendous growth and dominate technologies such as dosage error reductions platforms ($16 billion), clinical trial participant identifiers ($13 billion), preliminary diagnosis platforms ($5 billion), automated image diagnosis software ($3 billion). These trends point towards a demand for powerful AI tools that can interpret the constant inpouring of data from the rapidly mushrooming crop of pharma stakeholders, including those that are conducting of immuno-oncology trials. Artificial intelligence (AI)-based strategies are now being touted as an elegant solution to overcome several costly bottlenecks that slow down the progress of clinical trials. iNDX recognized the value of AI in managing and analyzing clinical trials data and systematically implemented the novel techniques in the iCore platform to make clinical trials more efficient.
Mohan Uttarwar, the CEO of the company announced that the company will now be called iNDX.Ai signifying the importance if AI and justifying the efforts that went into the development of iCore V4.0 – a cloud-based informatics platform and application suite that is designed to help translational researchers identify novel multiomic biomarkers in the clinical trials for immuno oncology. It touches the entire ecosystem of I/O research and empowers stakeholders to efficiently upload, track, view, manage, and analyze data within an integrated platform with open-APIs. It hosts a suite of powerful applications such as Integrated Correlation Engine (iCE) and iQuery which deploy artificial intelligence (AI)-, Machine Learning (ML)- and Natural Language Processing (NLP)-based approaches to conduct a correlative analysis of diverse multiomics and clinical datasets generated during translational studies and Biomarker discovery.
Vinayak Khattar. Ph.D., MBA.
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