Perspective: Foundation models for generalist medical artificial intelligence
The resulting models can raise self-explanatory warnings: “This patient is likely to develop acute respiratory distress syndrome, because the patient was recently admitted with a severe thoracic trauma and because the patient’s partial pressure of oxygen in the arterial blood has steadily decreased, despite an increased inspired fraction of oxygen”.
As a GMAI model may even be asked to provide treatment recommendations, despite mostly being trained on observational data, the model’s ability to infer and leverage causal relationships between medical concepts and clinical findings will play a key role for clinical applicabilityFinally, by accessing rich molecular and clinical knowledge, a GMAI model can solve tasks with limited data by drawing on knowledge of related problems, as exemplified by initial works on AI-based drug repurposingWe...
A solution needs to accurately interpret various radiology modalities, noticing even subtle abnormalities. Furthermore, it must integrate information from a patient’s history, including sources such as indications, laboratory results and previous images, when describing an image. It also needs to communicate with clinicians using multiple modalities, providing both text answers and dynamically annotated images.
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