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In low-resource healthcare settings, specialist shortages increase the need for accurate, fast interpretation that supports frontline decision-making. This study evaluates Sevamob LabReportAI, an AI tool that summarizes patient laboratory reports and suggests next steps by comparing AI outputs with expert physician consensus across 140 diverse cases. Results show high accuracy and reliability with potential workflow benefits and faster turnaround time.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/artificial-intelligence-based-lab-report-analysis-for-summary-and-recommendations-a-phase-2-field-study/253050/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/artificial-intelligence-based-lab-report-analysis-for-summary-and-recommendations-a-phase-2-field-study/253050.png","ImageObject",442,249,{"name":88,"@type":89},"Stanford","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-21","2026-09-13",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What problem does the study address in healthcare systems?","Question",{"text":108,"@type":109},"It addresses delayed or imprecise diagnosis and treatment in low-resource settings caused by limited specialist availability and the challenge of interpreting lab reports efficiently.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does Sevamob LabReportAI support frontline decision-making?",{"text":113,"@type":109},"It analyzes patient laboratory reports (including images, PDFs, and structured inputs) and generates summaries and suggested next steps to aid clinical decisions.",{"name":115,"@type":106,"acceptedAnswer":116},"How was LabReportAI evaluated and what were the key outcomes?",{"text":117,"@type":109},"The tool processed 140 lab reports and its outputs were compared with consensus analysis from expert physicians. Accuracy was high, with 133 of 135 evaluable cases showing concordance at 98.5%.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},253050,1789264082,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":76},2336477552062,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Artificial Intelligence based Lab Report Analysis for Summary and Recommendations: A Phase 2 Field  \nStudy  \nAnkit Agarwal1, Shelley Saxena2, Dr Ambarish Srivastava3, Vivek Mishra4, Balendra Singh5  \nDOI: 10. 29322/IJSRP.15.11.2025. p16735  \n[https://dx.doi.org/10.29322/IJSRP.15.11.2025.p16735](https://dx.doi.org/10.29322/IJSRP.15.11.2025.p16735)  \nPaper Received Date: 26th October 2025  \nPaper Acceptance Date: 25th November 2025  \nPaper Publication Date: 2nd December 2025  \nAbstract  \nIntroduction: The lab reports reveal what's happening inside the body, often before symptoms appear. They help doctors pinpoint the  \nexact cause of illness, reducing guesswork and misdiagnosis. They help in tracking the progression of chronic diseases. They show  \nwhether a treatment is working or needs tweaking. Artificial Intelligence (AI) can make it easier for both patients and frontline health  \nworkers to understand lab reports and evaluate next steps.  \nSevamob provides artificial intelligence assisted disease management platform to organizations. It has developed LabReportAI, an  \nartificial intelligence tool designed to analyze patient laboratory reports. The system generates both analysis and next steps to support  \nclinical decision-making by frontline health workers. To determine the accuracy of Sevamob LabReportAI, we conducted a clinical  \nstudy in which we used an Android smartphone/tablet with the Sevamob app. The app was operated by a nurse.  \nMethods: A total of 140 lab reports of patients with diverse case histories were included in this study. The reports included CBC,  \nblood chemistry, urinalysis and microbiology in PDF or image format. For each patient, the AI tool was provided the lab report along  \nwith age, gender and present symptoms of the patient and asked to summarize the report and suggest next steps. The AI output was  \ncompared with the consensus analysis from a panel of expert doctors who reviewed the same lab reports. The small sample size limits  \nthe generalizability and further studies are recommended for robust validation.  \nResults: Out of 140 cases, the AI system successfully provided responses in 134 cases (95.7%) . Six cases yielded no AI response due  \nto report quality or data parsing limitations or not sufficient details available to produce analysis and next steps. Of the evaluable  \ncases, concordance with the physician panel was observed in 133 out of 135 cases (98.5%) . Only one discordant (incorrect) response  \nwas noted.  \nConclusion: Sevamob LabReportAI demonstrates high accuracy and reliability, with potential for augmenting physician workflows in  \nroutine lab data interpretation while reducing turnaround time. It is particularly suitable for deployment in primary care and  \ncommunity health programs where expert doctors are scarce. Further large-scale validation studies are warranted to assess scalability  \nacross diverse patient populations and lab reporting formats.  \nIndex terms: Artificial intelligence, lab report analysis, predictive healthcare, medical diagnosis  \nArtificial Intelligence based Lab Report Analysis for Summary and Recommendations: A Phase 2 Field Study  \nIntroduction  \nHealthcare systems in low-resource settings face significant challenges in timely diagnosis and treatment due to limited availability of  \nspecialists. The lab reports are the body’s report card, quietly but critically guiding a patient’s care behind the scenes. They reveal  \nwhat's happening inside the body, often before symptoms appear. They help doctors pinpoint the exact cause of illness, reducing  \nguesswork and misdiagnosis. They help in tracking progression of chronic diseases. They show whether a treatment is working or  \nneeds tweaking. By understanding a lab report, frontline health workers and patients can take an informed decision about next steps.  \n[3] Artificial intelligence (AI) has shown great promise in augmenting triage and risk assessment across various specialties, including medicine","cbCaidwjAHsx5Mtn","https://ap.wps.com/l/cbCaidwjAHsx5Mtn","pdf",568040,9,"English","# Abstract\n## Introduction\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What problem does the study address in healthcare systems?\",\"answer\":\"It addresses delayed or imprecise diagnosis and treatment in low-resource settings caused by limited specialist availability and the challenge of interpreting lab reports efficiently.\"},{\"question\":\"How does Sevamob LabReportAI support frontline decision-making?\",\"answer\":\"It analyzes patient laboratory reports (including images, PDFs, and structured inputs) and generates summaries and suggested next steps to aid clinical decisions.\"},{\"question\":\"How was LabReportAI evaluated and what were the key outcomes?\",\"answer\":\"The tool processed 140 lab reports and its outputs were compared with consensus analysis from expert physicians. Accuracy was high, with 133 of 135 evaluable cases showing concordance at 98.5%.\"}]","Artificial Intelligence based Lab Report Analysis for Summary and Recommendations - A Phase 2 Field Study | PDF"]