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Benjamin Senst@bsenst
🤝 Top Collabolators
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🤓 Submissions
Llama-2 Reasoning Support
Clinical reasoning is at the center of medical care. History taking and physical examination findings provide the greatest part to identify a preliminary list of diagnoses. But clinical reasoning suffers from bias. Supporting reasoning through large language models might help healthcare providers in diagnostic thinking. As initial step a list of possible diagnoses is created from a symptoms. In the future chained prompts might additionaly produce suggestions for further diagnostic workup based on the list of differential diagnosis or provide key findings that help to differentiate between the preliminary list of diseases.
everdiver
Evernote is used by 250 million people worldwide to collect ideas, document meetings, and much more. It runs as a local application or in the cloud and has preceded comparable applications by the big tech companies. Once the note collection has grown over the years, it can become cumbersome to keep an overview and refer to past notes. Large language models offer help to improve search functions and build knowledge from old notes. With the dawn of open-source large language models such as Falcon by the Technology Innovation Institute, new possibilities have been opened. Since these models can be run on less powerful customer hardware, private Evernote data can now be processed, preserving privacy.
