Medical AI Doesn’t Just Need Bigger Models. It Needs an ImageNet for State Transitions
The article discusses the need for a new dataset in medical AI, analogous to ImageNet, to facilitate understanding biological state transitions. It emphasizes that current medical AI models focus on classification and question answering rather than on predicting future states based on interventions. The author argues that developing a shared infrastructure for recording and evaluating biological state transitions is crucial for advancing medical AI.
- ▪Medical AI is evolving beyond classification and risk prediction to focus on state transitions.
- ▪A new dataset, referred to as Biomedical TransitionNet, is proposed to standardize and evaluate biological state transitions.
- ▪Current medical AI systems lack sufficient transition data to effectively understand and influence biological trajectories.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3919256) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } JXIONG Posted on May 19 Medical AI Doesn’t Just Need Bigger Models. It Needs an ImageNet for State Transitions #ai #worldmodel #steeramed #steerability Whoever builds the “state–intervention–transition” dataset for biomedicine may define the next generation of medical AI infrastructure. Author: Jianghui Xiong Medical AI is moving beyond classification, risk prediction, and question answering.
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