Summarizing Clinical Research Papers

Healthcare professionals are often challenged to keep up with the vast amount of scientific research due to time constraints and the large volume of literature available. They recognize the importance of staying updated with the latest medical literature to provide evidence-based care. However, the process of searching, reading, and evaluating research papers is time-consuming and frustrating when doctors find that the literature they invested time in doesn't meet the criteria of relevance or quality. The limited time for literature review, coupled with the difficulty in discerning credible and relevant papers, results in wasted efforts, delayed access to valuable information, and potential gaps in knowledge that can affect patient care.

• Doctors spend approximately 21 hours a day reading on scientific updates, limiting time for thorough literature review [1].
• Primary care physicians face a substantial amount of literature, with over 7,000 articles published monthly in primary care journals alone [2].
• Clinical studies often vary widely in terms of design, patient populations, interventions, and outcomes. This heterogeneity can complicate the synthesis of data and the drawing of meaningful conclusions in systematic reviews [3].

An AI digital health assistant to streamline the literature review process. By leveraging language processing capabilities, this tool automatically analyzes and extracts highlights from medical research articles, delivering concise summaries that capture research objectives, methodologies, findings, and implications. This is particularly useful in systematic reviews and meta-analyses, where large datasets need to be integrated and analyzed.

Moreover, it reduces the time to find high-quality evidence from reliable repositories such as PubMed, MedQA, and MedMCQA.

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• Finds evidence-based information 4.4x faster, freeing up time for healthcare professionals to focus on patient care and decision-making.
• AI systems can easily scale to handle increasing volumes of data, which is necessary as the amount of published research continues to grow.
• Improve cost-effectiveness by automating routine tasks, reducing the need for manual labor, and minimizing errors [4].