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

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