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2019) use transfer learning where a pre-trained model (e.g. through self supervision of learning a language model) is fine tuned with a labeled dataset. 2020): Medical summarization warrants high precision and therefore the summarizer should be good at capturing all the medical information (medications, symptoms etc) discussed in the dialogue and (2) discern all the affirmatives and negatives on medical conditions correctly (e.g. no allergies, having a cough for 2 days). They may put on weight due to abnormal heart conditions. Privacy Concerns: At inference time, an API call to external services such GPT-3 may not always be possible due to HIPAA and privacy concerns.