Day 2 Hall 3

Across industries, enterprise leaders are ramping up digital initiatives at an unprecedented pace, reinventing how we live and work. But as these projects get underway, it has been witnessed that roughly only half of all AI proofs of concept ever make it to production. The complexity of ML implementation varies with increasing degrees of expectation from ML infrastructure, model complexity, and deployment. At its core, machine learning operations (MLOps) help teams consistently develop, deploy, monitor, and scale AI and ML models, mitigating the potential risks associated with not having a framework for sustainable innovation.

Within healthcare, technologies such as AI/ML, predictive analytics etc. can be applied to augment human tasks. This may involve assisting disease progression management, tailoring care decisions to individuals, early identification of disease or utilization, improving engagement and adherence, and improving efficiency and consistency of administrative processes. In this session we look at the benefits and pitfalls of using digital health solutions in building a truly modern health care ecosystem.

Building a clinical search engine that thinks and works the way you do, making it easier to find and apply relevant knowledge. Empower clinicians and students to make faster, better decisions and avoid errors at the point of care through access to the most complete and trusted evidence-based content. Sneak peak of the search functionality of Clinical Key offerings.

Datasciences profession has been tagged with some rather cliched terms and is often in limelight for the right reasons of its contribution in powering product and experience differentiation.

While many industry segments and enterprises of varied scale have setup data sciences teams, the focus of development has primarily been on technical skills.

Join this presentation by industry and academia veterans to decipher how to craft a holistic success profile of a rockstar datascientist and leveraging it to power employee development.

The talk would discuss about challenges w.r.t data and quality of AI models in healthcare – how can we build high quality AI models in healthcare with less data: the challenges and opportunities. How building AI models in health care is different from other industries.