Is public health in the era of deep machine learning?
The use of AI-generated imagery is an opportunity to narrow educational gaps in general public health and provide an avenue to deliver affordable microbiology imagery to educate audiences across the world. Generative image models are trained on large datasets to enable the generation of synthetic images that closely resemble what happens at the molecular and cellular levels in our bodies. Thus we train algorithms in deep machine learning. The output use cases range from educational use to image processing in a clinical setting, such as for medical diagnostics or accelerated research and development. Here are two key challenges for the industry ahead.
Data bias: Generative AI models rely heavily on the training data's quality and diversity. If the training data is biased or limited in representation, the generated images may inherit these biases, potentially leading to inaccurate images or, in a clinical setting, a misdiagnosis.
Explainability: Deep learning models, including generative AI models, are still considered "black boxes" due to their complex architectures and internal workings. The lack of interpretability and explainability challenges understanding how and why a model generated a specific image. Addressing this limitation is crucial for building trust in generative AI models in the public healthcare industry.
Will generative AI art disrupt the “stock imagery” industry?
Historically, stock image libraries have been an important resource for education initiatives in public health. Even though it’s in the early stages, there are already some changes disrupting this space. Getty Images recently launched a generative AI art tool trained on its content library. Other companies in the field are doing the same. Adobe released its Firefly model, trained on its licensed images, across its Creative Suite and Creative Cloud service.
Using the AI capabilities directly within the stock photo sites requires an additional subscription to use the tools. While in beta, these tools did not compare well to Midjourney for quality and thus were not used in the creation of the images illustrated above. The Firefly tools in the latest releases of Photoshop and Illustrator are used mainly for adding background to existing photos or illustrations to accommodate multiple crop sizes for the image creation team’s content management system.
Here are some of the benefits of using generative AI in public health education for government agencies.
- Automation: Generative AI can automate the creation of customized educational content tailored to specific healthcare topics, ensuring that the material is relevant and engaging for different learners
- Scalability of knowledge: Government agencies can quickly generate a vast array of learning resources, including interactive simulations, case studies and assessments, to accommodate greater citizen learning
- Cost-effectiveness: Generative AI can significantly reduce the time and resources required for content creation. Automated generation of educational materials allows government agencies to develop and update training content more efficiently, saving both time and costs
Now is the time to revolutionize the way public health agencies educate their audience.
Using AI to create images for communication and educational purposes has the potential to revolutionize the way public health agencies communicate with and educate citizens.