Google researchers wrote, “An important focal area for future exploration is the integration of the responsible AI principles throughout the model development process, including, but not limited to, the principles of fairness, privacy, equity, transparency, and accountability.”
Google has recently announced the creation of Med-Gemini, a new series of Artificial Intelligence models tailored specifically for the healthcare sector.
Developed by Google’s top AI researchers and engineers, including significant input from Jeff Dean, Chief Scientist at Google DeepMind and Google Research, these models are still in the research phase and not yet available to the public.
Results from our first ✨Med-Gemini✨models, variants of the base Gemini multimodal models that have been tuned for the medical domain.
The multimodal and long context capabilities of the Gemini models really shine in some of the capabilities.
Joint work with @vivnat… https://t.co/KkKdLXjBx7
— Jeff Dean (@🏡) (@JeffDean) April 30, 2024
Med-Gemini consists of various models such as Med-Gemini-L 1.0, Med-Gemini-M 1.0, Med-Gemini-M 1.5, and Med-Gemini-S 1.0. These models are designed to enhance clinical decision-making by integrating Multimodal capabilities that can process text, images, and video data.
This integration enables the AI to perform complex clinical reasoning tasks with increased accuracy and reliability.
Jeff Dean, Chief Scientist of Google DeepMind and Google Research, said on X, “I’m very excited about the possibilities of these models to help clinicians deliver better care, as well as to help patients better understand their medical conditions. AI for healthcare is going to be one of the most impactful application domains for AI, in my opinion.”
While Google announced these developments last month, it has not provided a specific public release date, indicating that further improvements are needed before the models can be made available.
The research and development of these models are being conducted at Google’s headquarters in California, in collaboration with various global medical research institutes.
The utilization of AI in healthcare has been the talk of the town for months and people hold different views and opinions about it.
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The goal of Med-Gemini is to provide clinicians with better diagnostic tools and to help patients understand their medical conditions more effectively. The AI models are particularly adept at handling long-context interactions, which are essential for interpreting extensive medical records and complex health data.
They build on the foundation of Google’s existing Gemini 1.0 and 1.5 large language models and are enhanced with self-training techniques to improve factual accuracy and nuance when presenting medical information.
Google reports that the Med-Gemini AI models have excelled in text-based reasoning tasks on the GeneTuring dataset and have achieved a 91.1% accuracy rate on MedQA (USMLE), significantly improving upon their predecessor, Med-PaLM 2, by 4.5%.
Some seemed to question Google about the utilization of LLM in medicine.
This is very interesting work. I followed AIME closely.
I am surprised there is not more buzz around this. When do you think we will start seeing large-scale public pilots of LLM assistance in medicine?
— Loss Landscape (@LandscapeL26502) May 1, 2024
These results highlight the potential of AI to revolutionize healthcare by providing tools capable of interpreting complex medical data with unprecedented accuracy.
However, Google emphasizes that more testing and development are necessary to ensure the efficacy and safety of these models in real-world clinical settings. The company is committed to integrating responsible AI principles throughout the development process to ensure fairness, privacy, equity, transparency, and accountability.
Many seemed eager for innovations like these in the healthcare industry.
Yes please. Hopefully costs come down with this.
— Bob Swinson (@bobswinson) May 1, 2024
As the medical industry continues to evolve, Google’s introduction of the Med-Gemini AI models marks a significant advance in the application of artificial intelligence to enhance health outcomes.
This technology, once validated and released, could potentially reduce the cognitive load on clinicians and augment their capabilities, thereby enhancing the overall quality of patient care.
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