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Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated in clinical settings different from the training environment. A common mitigation strategy is to develop separate systems for each clinical setting using site-specific data [1]. However, this quickly becomes impractical as medical data is time-consuming to acquire and expensive to annotate [2]. Thus, the problem of "data-efficient generalization" presents an ongoing difficulty for Medical AI development. Although progress in representation learning shows promise, their benefits have not been rigorously studied, specifically for out-of-distribution settings. To meet these challenges, we present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. REMEDIS uses a generic combination of large-scale supervised transfer learning with self-supervised learning and requires little task-specific customization. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data. REMEDIS exhibits significantly improved in-distribution performance with up to 11.5% relative improvement in diagnostic accuracy over a strong supervised baseline. More importantly, our strategy leads to strong data-efficient generalization of medical imaging AI, matching strong supervised baselines using between 1% to 33% of retraining data across tasks. These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.

Background. Radiation recall reactions, in particular dermatitis, are well documented in the literature. However, radiation recall mucositis is a rare clinical phenomenon.Methods. We report a case of a 45-year-old man diagnosed with squamous cell carcinoma of the base of tongue. He was treated with surgery followed by chemotherapy and radiation therapy. Several months after completing treatment, he had a recurrence develop outside of the previously irradiated field. He was offered radiation therapy concurrent with docetaxel as salvage therapy.Results. During salvage therapy, acute recall mucositis developed corresponding to his previously irradiated fields. His chemotherapy with docetaxel was withheld, and his symptoms rapidly improved.Conclusions. This case describes radiation recall mucositis associated with docetaxel, a rare but potentially serious clinical situation. Given the potential severity of the reaction and increasing use of docetaxel as second-line treatment of recurrent head and neck cancers, it is important to be aware of this phenomenon. B

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