Does content-adaptive tokenization enable more efficient inference latency per token processed compared to fix

Abstract

Abstract: The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or

Research Question

Does content-adaptive tokenization enable more efficient inference latency per token processed compared to fixed-patch approaches on vision-language medical AI benchmarks?

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