AI Articles and Analysis about Case management

Case management is the coordination of services on behalf of an individual person who may be considered a case in different settings such as health care, nursing, rehabilitation, social work, disability insurance, employment, and law.

The Impact of AI on Drug Target Discovery and the Personalization of Healthcare – with Leo Barella of Takeda@2x-min
"Clinical support
Case management
Claim management
Claim management
Customer analytics
Customer service
Data analysis
Data collection
Data management
Enterprise resource planning
Predictive analytics and forecasting

The Impact of AI on Drug Target Discovery and the Personalization of Healthcare – with Leo Barella of Takeda

AI has profound implications for drug discovery, ushering in a new era of innovation and efficiency. Recent milestones - such as the first AI-designed drug molecule to enter human clinical trials and the prediction of protein structures for millions of proteins - showcase the transformative power of AI in this domain.

The Future of Healthcare in Large Language Models@2x-min
Business intelligence and analytics
Healthcare
Case management
Healthcare services
Data auditing
Data collection
Data management
Enterprise resource planning
Large language models
Predictive analytics and forecasting

The Future of Healthcare in Large Language Models – with Ylan Kazi of Blue Cross

In just the last year, rapid advancements in AI technologies, particularly in natural language processing (NLP), have dramatically impacted virtually every industry. Most recently, large language models (LLMs) have become front and center – driven by the overwhelming popularity of OpenAI's ChatGPT. 

OECD-Approved AI Tools and Resources for Financial Services@2x
Business intelligence and analytics
Case management
Compliance
Customer analytics
Data analysis
Data auditing
Data collection
Data integration
Data management
Enterprise resource planning
Knowledge management
Predictive analytics and forecasting
Report generation
Text analysis

OECD-Approved AI Tools and Resources for Financial Services

The OECD.AI Policy Observatory is an inclusive platform that brings together resources and expertise from the OECD and its partners to facilitate dialogue and provide evidence-based policy analysis on the impact of AI. It is built upon the foundation of the OECD AI Principles, the first intergovernmental standard on AI adopted in 2019, endorsed by OECD countries and partner economies.

Strategic Adoption of Large Language Models and Generative AI – with Asif Hasan of Quantiph@2x
Case management
Data collection
Data entry
Data management
Discovery
Drug discovery
Employee engagement
Enterprise resource planning
Knowledge management
Legal research
Machine learning infrastructure
Process automation
Research and development
Risk management
Software development

Strategic Adoption of Large Language Models and Generative AI – with Asif Hasan of Quantiphi

As nearly every American household has realized over the last year, large language models combined with generative AI abilities pose tremendous challenges and opportunities for enterprises of every shape and size. Just ask anyone who has heard of ChatGPT.

Intelligent Automation for Enhancing RPA in Banking@2x-min
Automation and robotics
Business intelligence and analytics
Case management
Claim management
Data auditing
Data collection
Data entry
Document processing
Process automation

Intelligent Automation for Enhancing RPA in Banking – Two Use Cases

Critical to the definition of robotic process automation (RPA) is the notion that the tasks a 'robotic' software automates are repetitive by nature, with exceptions in rare instances. While RPA cannot independently learn from and adapt to new contexts and workflow problems, it can if the RPA system is imbued with the correct AI capabilities. 

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What Enterprises Can Learn from How Google Models AI Readiness@2x-min
Business intelligence and analytics
Case management
Data analysis
Data auditing
Data collection
Data integration
Data management
Enterprise resource planning
Knowledge management

What Enterprises Can Learn from How Google Models AI Readiness – with Pallab Deb of Google Cloud

It is exceptionally difficult to get started, much less succeed, in AI adoption if one does not have at least a foundational education, particularly in those “modules” pertaining to AI capabilities, requirements, and organizational readiness. A business wanting to implement AI must understand the current state of adoption and how this current state matches up against AI readiness requirements.