However, it's important to note that real-world validation on actual production-level data in everyday clinic settings or a prospective double blind randomized clinical trial has not yet occurred. Despite leveraging diverse data sources, including health records, X-rays, and medical exam prep questions, the burden of proof for these multimodal models' effectiveness in real-life clinical settings remains.
Key takeaways:
- Google and DeepMind have released a paper describing Med-Gemini, a group of advanced AI models targeting healthcare applications, which are claimed to outperform competing models such as OpenAI’s GPT-4.
- Med-Gemini's unique feature is its ability to capture context and temporality, understanding the background and setting of symptoms as well as the timing and sequence of their onset.
- Instead of building a general medical model, Google's developers have adopted a vertical-by-vertical approach, creating a 'family' of models each optimizing a specific medical domain or scenario.
- Despite the advancements, it is highlighted that real-world validation on actual production-level data in an everyday clinic setting, or at least a prospective double blind randomized clinical trial, has yet to happen.