Summary of Question Answering Systems For Health Professionals at the Point Of Care — a Systematic Review, by Gregory Kell et al.
Question answering systems for health professionals at the point of care – a systematic review
by Gregory Kell, Angus Roberts, Serge Umansky, Linglong Qian, Davide Ferrari, Frank Soboczenski, Byron Wallace, Nikhil Patel, Iain J Marshall
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this systematic review, researchers aim to improve question answering (QA) systems in healthcare by characterizing current medical QA systems, assessing their suitability for healthcare, and identifying areas for improvement. The goal is to develop more effective QA systems that provide health professionals with the latest and most relevant evidence, ultimately enhancing clinical care quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Question answering (QA) systems can help healthcare professionals get the latest information they need, making patient care better. But these systems haven’t been widely used yet. This study looks at what’s available now, if it works for healthcare, and where things could be improved to make QA systems more helpful in hospitals. |
Keywords
* Artificial intelligence * Question answering
