AI case study

Accelerate clinical research

by
Ospedale San Raffaele
Context

Ospedale San Raffaele developed a cloud-based platform powered by AI and Machine Learning to transform clinical research and precision medicine. Predictive models and generative AI tools help analyze complex data, improve decision-making, automate low-value activities, focusing on research, diagnosis, and treatment.

Results

Supports clinical studies on various diseases, helping medical teams assess risk and predict patient response to treatment.

Results not reported in the source
Industry
Healthcare
Region
Europe
Published
October 23, 2024
Agent type
Data Agents
AI provider
Microsoft
Models/tools
Not disclosed
ICE score
384
The ICE framework in this database provides a quick way to assess the feasibility and potential impact of AI use cases, with higher scores signaling more actionable opportunities.

Impact: Potential benefits to the business.

Confidence: Likelihood of achieving expected results.

Ease: Simplicity of implementation in terms of resources and time.

ICE Score: Calculated by multiplying the component scores.

Note:
Each score is AI-generated based on available data and should be viewed merely as a general guideline for deeper exploration of the use cases.
Impact
8
Confidence
8
Ease
6

77

AI use cases in

Healthcare

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Cactus Life Sciences

Healthcare
Use case
Faster scientific content creation
Context

Cactus Life Sciences implemented Microsoft 365 Copilot to automate routine tasks and augment the generation of scientific content under human oversight. They integrated the tool into their Microsoft 365 workflows to assist with drafting, editing, and approving complex scientific communications, streamlining content creation and dissemination processes. This approach improved the efficiency of internal content workflows enabling faster communication of critical scientific data to stakeholders.

Models/tools

Indegene

Healthcare
Use case
Time-intensive admin tasks
Context

Indegene integrated Microsoft 365 Copilot into its suite of productivity tools, including Word, Excel, PowerPoint, Outlook, and Teams, to automate routine email responses, document summarization, data analysis, and RFP development. The solution was implemented across departments such as content, pre-sales, finance, and project management, ensuring stringent data security and privacy standards while streamlining critical business workflows.

Models/tools

Ontada

Healthcare
Data Agents
Quick win
Use case
Faster oncology data extraction
Context

Ontada leveraged Microsoft’s Azure OpenAI Service Batch API and Azure AI Foundry to build its ON.Genuity platform, which processes 150 million unstructured oncology documents to automatically extract nearly 100 critical data elements across 39 cancer types. They integrated the new platform with their structured iKnowMed system using Azure Databricks for data ingestion and Azure Document Intelligence for text extraction, transforming manual chart review processes into an automated workflow that supports clinical decision-making and life science product development.

Explore industries

151

companies using

Data Agents

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Use case
Scalable contract detection
Context

wealthAPI implemented a next‐gen contract detection solution by integrating DataStax Astra DB on Google Cloud and leveraging Google Gemini models for AI‐powered analysis. They deployed DataStax’s vector search and real‐time insights capabilities to scale contract detection across millions of users in less than three months, streamlining wealth management workflows by dramatically reducing response times and efficiently handling massive data volumes.

Models/tools
...
1
Use case
Automated job title classification
Context

Aura Intelligence integrated Anthropic's Claude via Amazon Bedrock into its data pipeline to automatically classify over 200 million job titles and industry pairings from multi-language data, replacing manual lookups and fuzzy matching. They fine-tuned foundation models on proprietary datasets and leveraged AWS infrastructure, including SageMaker and prompt management, to automate QA, report generation, anomaly detection, and real-time hiring trend analysis.

Models/tools
...
2
Use case
Efficient engineering team management
Context

LaunchNotes leverages Claude in Amazon Bedrock in their product 'Graph' to transform engineering data into actionable insights. Graph functions as an ETL platform with Claude managing data pipelines, helping engineering managers understand development metrics, reduce incident identification time, automate updates, and generate customized release notes and technical documentation.

Models/tools
...
2
Explore agents

251

solutions powered by

Microsoft

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Use case
High-volume student queries
Context

Physics Wallah developed 'Gyan Guru', a hyperpersonalized conversational study companion to address the unique academic and support needs of its 2 million daily users. The system was implemented by indexing over one million Q&As and ten million solved doubts in a vector database, then leveraging a Retrieval-Augmented Generation (RAG) approach integrated with Azure OpenAI to deliver individualized, context-aware responses. This integration streamlined various student interactions including academic queries, product-related issues, and general support, reducing reliance on human subject matter experts.

Models/tools
...
1
Use case
Streamlined executive operations
Context

Brandix adopted the Microsoft 365 Copilot suite via the Early Access Program to integrate AI into key executive workflows, automating meeting transcript-based action tracking in Teams, accelerating pitch deck creation in PowerPoint, and streamlining document generation in Word while leveraging AI-enhanced querying in Power BI and efficient information retrieval via Copilot Chat. This implementation transformed internal communications, reporting, and presentation preparation processes by significantly reducing manual effort.

Models/tools
...
2
Use case
Future-proof IT operations
Context

University of Oxford’s IT Services department deployed Microsoft 365 Copilot across its 400+ staff as part of a pilot program aimed at embedding generative AI into research, teaching, and administrative workflows. They implemented extensive onboarding sessions, community engagement via Microsoft Teams, and established an acceleration team to integrate Copilot with other business applications. This deployment streamlined routine tasks such as document generation, summarization, and meeting support, fostering innovation within the department.

Models/tools
...
1
Explore AI providers

159

AI use cases in

Europe

See All
Use case
Faster research reports
Context

Deutsche Bank developed DB Lumina, an AI-powered research agent built on Gemini and Vertex AI through a partnership with Google Cloud. The solution automates the creation of financial research reports by rapidly condensing extensive market data—such as converting a 400-page report into a three-page summary—thereby streamlining analysis workflows while maintaining rigorous data privacy standards.

Models/tools
...
2
Use case
Optimized CX workflows
Context

Capgemini partnered with Google Cloud to develop industry-specific agentic AI solutions that automate customer request handling across multiple channels such as web, social, and phone. The implementation integrates Google Agentspace, Customer Engagement Suite, and Agent2Agent interoperability protocol into existing customer service infrastructures to enhance personalized support, call routing, and workflow automation. This advanced solution transforms customer experience by streamlining communications and enabling proactive engagement.

Models/tools
...
3
Use case
Scalable contract detection
Context

wealthAPI implemented a next‐gen contract detection solution by integrating DataStax Astra DB on Google Cloud and leveraging Google Gemini models for AI‐powered analysis. They deployed DataStax’s vector search and real‐time insights capabilities to scale contract detection across millions of users in less than three months, streamlining wealth management workflows by dramatically reducing response times and efficiently handling massive data volumes.

Models/tools
...
1
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Thoughts & ideas