The ever swifter, wider use of AI in healthcare research makes for many mistakes along the way and often yields inherently flawed research, explains ML developer and medical student Moein Shariatnia, who devises computer vision and NLP applications in healthcare with DL models.

Unlike most AI, which focuses on and is trained for handling just one specific modality, multimodal AI models can handle different modalities such as image, text, and speech at the same time, in addition to narrow and specific tasks. This means that, if and when multi-modality AI progresses to the point where it can deploy on a vast scale, it will truly revolutionize medicine and many other fields.

Generative models are hot items in tech news today. Stable Diffusion and Midjourney - which create amazing artwork - and ChatGPT, which writes excellent essays for an input prompt - have astounded many. Similar models are also in the works for healthcare - especially for radiology - to address their real-world challenges. ML developer and medical student Moein Shariatnia sketches a brief history of deep generative models and explains how they work and their use in medical imaging.

Google has some of the most formidable AI capabilities in the world, though among their applications diabetic retinopathy is not the first that springs to mind. As diabetics are likelier to develop eye-related disorders and their numbers continue to grow worldwide, this is extremely valuable.