Summarizing Medical Charts
Sector: Healthcare
Capability: Summarization
Steve, a generative AI specialist based in New York City, collaborates with leading healthcare organizations such as Weill Cornell. His task is to assist in summarizing their large volumes of medical documents - a process previously time-consuming and expensive, often requiring third or fourth-year medical students to manually write summaries for each patient chart.
Facing challenges with medical chart summarization due to its intensive demand on resources and time, Steve estimated a considerable annual expenditure to summarize thousands of charts. However, with Anote, a provider of renowned annotation solutions, Steve was able to address these issues. Anote offered an annotation interface that integrated seamlessly with existing systems, allowing medical charts to be uploaded and AI model-driven questions to customize the summaries.
Upload Data
Start by uploading the medical charts in the Upload Unstructured format, choose the NLP task of Prompting, and choose the document decomposition.
Customize Questions
Input the relevant questions for the medical charts.
Annotate
Insert human feedback via inputing the relevant answers for the questions in the medical charts.
As we annotate more medical charts, the model is able to learn better summaries over time.
Download Results
When finished annotating, you can download the results from the medical charts. Below, you can see both the predicted results from the model, as well as the actual answers from the annotators / subject matter experts.
