Generative AI Use Case Library for the Healthcare Industry
Identify value-driven generative AI use cases to transform your organization.
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×- AI in healthcare adoption has been slow despite rapid growth in investment and AI product development.
- Healthcare organizations are challenged with inefficient business operations and are uncertain on how to effectively address them.
- Adopting new technology requires a strategic approach and alignment between IT and healthcare administrators.
Our Advice
Critical Insight
The healthcare industry should approach a Gen AI adoption strategically and methodically within a larger digital strategy, with a clear understanding of the specific use cases and benefits.
Impact and Result
Info-Tech’s use-case library provides practical guidance to help the healthcare industry accelerate value-driven Gen AI use case adoption.
Generative AI Use Case Library for the Healthcare Industry Research & Tools
1. Gen AI Use Case Library for Healthcare – Identify value-driven generative AI use cases to transform your organization.
Leverage a robust set of Gen AI use cases to identify potential initiatives that can accelerate value creation. Use cases cover a range of technologies and capabilities and include insight into sources of value.
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Generative AI Use Case Library for the Healthcare Industry
Identify value-driven generative AI use cases to transform your organization.
Analyst Perspective
Implement value-driven AI use cases responsibly.
Healthcare is becoming familiar with artificial intelligence (AI) and has been piloting several use cases in clinical decision making, medical imaging, and others with various AI modalities. AI can serve many purposes, including diagnosis and treatment, drug development, remote patient monitoring, and predictive analytics. Healthcare providers can analyze medical data with AI to make more accurate diagnoses, develop more effective treatment plans, and monitor patients more effectively.
However, the adoption of AI in healthcare poses some challenges. One main challenge is ensuring that the data used to train AI algorithms is accurate and representative of the population being served. Another challenge is ensuring that AI is used ethically and responsibly, with appropriate safeguards in place to protect patient privacy and prevent bias. Health organizations should approach AI adoption strategically, with a clear understanding of the specific use cases and benefits, and a plan for addressing the challenges associated with implementation and ongoing use.
As healthcare organizations consider implementing AI, we recommend a rapid approach following a six-step framework to help our members adopt value-driven AI use cases which consider risks and limitations.
Sharon Auma-Ebanyat
Research Director, Healthcare
Industry Practice
Info-Tech Research Group
Executive Summary
Your Challenge | Common Obstacles | Info-Tech's Approach |
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Info-Tech Insight
Healthcare organizations should approach AI adoption strategically and responsibly, with a clear understanding of the specific use cases and benefits, and a plan for addressing the challenges associated with implementation and ongoing use. Healthcare organizations that invest in AI technology will foster innovation to improve operational efficiency and patient outcomes.
Gen AI is an innovation in machine learning
Generative AI (Gen AI)
A form of machine learning where, in response to prompts, a Gen AI platform can generate new outputs based on its training data. Depending on its foundational model, a Gen AI platform will provide different modalities and use case applications.
Audio - Converts text to sound.
Visual - Enables text to image, video, or web design conversions.
Code - Creates code in various programming languages based on human language prompts.
Text - Creates text-based outputs such as articles, blog posts, emails, and information summaries.
Machine learning (ML)
An approach to implementing AI where the AI system is instructed to search for patterns in a data set and then make predictions based on those patterns. In this way, the system "learns" to provide accurate content over time (e.g. Google's search recommendations).
Artificial intelligence (AI)
A field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior - many systems operate on preset rules, such as customer service chatbots.
Info-Tech Insight
Many vendors have jumped on Gen AI as the latest marketing buzzword. When vendors claim to offer Gen AI functionality, pin down what exactly is generative about it. The solution must be able to induce new outputs from inputted data via self-supervision - not just trained to produce certain outputs based on certain inputs.
Healthcare may not feel prepared, but other industries are already using AI
"The next 5-10 years will see significant adoption of AI technologies in the nonprofit sector. The low hanging fruit will be around making routine tasks more efficient to allow people to work smarter, not harder."
- Nathan Chappell, Futurus Group
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Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.
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A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.
Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.
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Sharon Auma-Ebanyat
Contributors
- Amy Feaster, CDO & VP of IT Children’s Hospital Colorado
- Bill Wong, Principal Research Director, Info-Tech
- Irina Sedenko, Research Director, Info-Tech
- Neal Rosenblatt, Principal Research Director, Info-Tech
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Search Code: 102952
Last Revised: October 17, 2023
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