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What Leaders Want: Shifting to AI-Driven Healthcare

DataRobot Blog

In our previous healthcare blog , Sally Embrey explained how the integration of health and care services is gathering pace globally and how the creation of Integrated Care Systems (ICSs) by England’s National Health Service (NHS) is the latest example of services being organized around a local population. Learn More.

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Financial Data & AI: The Future of Business Intelligence

Defined.ai blog

The following section will explore the potential challenges of integrating AI and financial data and discuss strategies to overcome them. Overcoming Challenges in AI and Financial Data Integration As with any technological advancement, integrating AI and financial data presents its own set of challenges.

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Deep Learning for Medical Image Analysis: Current Trends and Future Directions

Heartbeat

Explainable AI and Interpretability The decision-making process of deep learning models is unintelligible and inexplicable, making medical picture interpretation difficult. This section will explore some of these directions and technologies, highlighting their potential impact on the field.

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

Security and privacy —When all data scientists and AI models are given access to data through a single point of entry, data integrity and security are improved. Key to explainable AI is the ability to automatically compile information on a model to better explain its analytics decision-making.

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Achieve competitive advantage in precision medicine with IBM and Amazon Omics

IBM Journey to AI blog

Processing terabytes or even petabytes of increasing complex omics data generated by NGS platforms has necessitated development of omics informatics. gene expression; microbiome data) and any tabular data (e.g., clinical) using a range of machine learning models.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Data storage and versioning You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment. Easy collaboration, annotator management, and QA workflows.