Real-Time Medical Text Review
Similar to a medical version of “Grammarly,” our solution automatically detects errors in medical texts and provides correction suggestions.
Docly leverages 1.4 billion global healthcare data points and clinical reasoning technology to develop an AI solution that detects errors in electronic health records (EHR) and medical texts. Our goal is to eliminate AI hallucinations and inaccuracies, enhancing the precision of medical documentation while providing healthcare teams with real-time detection and recommendations to improve their efficiency and the quality of patient care.
Docly’s vision is to become the global leader in real-time error detection for generative AI medical summaries, focusing on healthcare professionals to enhance the quality of care and the accuracy of medical decision-making.
Real-time detection of errors, omissions, and irrelevant information in generative AI-generated medical summaries ensures precise medical records, thereby reducing the risk of medical errors.
We assist healthcare providers in quickly proofreading generated medical content, eliminating the need to repeatedly read extensive documents and allowing them to focus on software correction prompts, effectively enhancing work efficiency.
By implementing the Docly medical error detection model, we enhance physicians’ confidence in generative AI technology, promoting its widespread application in the healthcare sector.
We help healthcare institutions improve the completeness and accuracy of medical records, which not only enhances diagnostic accuracy but also reduces the likelihood of record deletions and medical disputes.
Similar to a medical version of “Grammarly,” our solution automatically detects errors in medical texts and provides correction suggestions.
Through validation by clinical experts and data verification, we provide correction suggestions and real-time prompts with an accuracy rate of up to 93%.
Using real-world medical databases as a foundational model, we employ retrieval-augmented generation (RAG) and efficient parameter fine-tuning to effectively detect AI hallucinations and omissions.
Our solution covers multiple fields, including internal medicine, surgery, ICU, and pediatrics, meeting diverse clinical needs.
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