 Aesop Technology tackles a different challenge: information overload. Founded as a research project at Taipei Medical University (TMU) in 2011, Aesop began by detecting prescription errors in Taiwan’s vast NHI database. Today, it has evolved into a comprehensive clinical-decision platform that uses AI to guide doctors through complex treatment pathways in real time. Aesop Technology cofounder and Chief Product Officer Jeremiah Scholl argues that some of the most transformative uses of AI come from decision support. “Our AI doesn’t tell doctors what to do — it helps them find the information they are looking for to make decisions much faster,” he says. Embedded directly into hospital electronic records, Aesop’s software, named 𝗠𝗲𝗱𝗶𝗴𝗮𝘁𝗼𝗿, automatically loads the latest U.S. National Comprehensive Cancer Network (NCCN) cancer-treatment guidelines, cross-checks them with patient data, and provides physicians with the most recent published evidence that is relevant for their patient. Of the products Aesop offers, Medigator continuously scans journals and clinical-trial databases, updating recommendations within hours of new findings — a process Scholl describes as “bringing the latest data quietly into the doctor’s workflow.” The platform was made possible through the national data exchange already established in Taiwan, which has allowed physicians to share patient data securely across hospitals. The foundational research projects at TMU obtained access to this data to develop the AI models it uses, showing how Taiwan’s healthcare infrastructure is shaping the island into an AI-enabled healthcare powerhouse. At a broader level, Aesop’s analytics evaluate how well hospitals adhere to international standards and where real-world results diverge, giving tumor boards and research partners a clearer picture of where care can improve. By connecting guidelines with real outcomes, Scholl says, Taiwan’s hospitals can turn overwhelming data into practical insight — “making accuracy routine, not exceptional.” *This is an excerpt from the article.
DxPrime, an AI-powered solution designed to enhance diagnostic accuracy through real-time integrity analysis and automated code translation, is now available on Mayo Clinic Platform. Driven by AESOP Technology's Clinical Deep Reasoning Network Model, DxPrime customizes its insights based on specialty, gender, and clinical context to ensure diagnostic completeness and precision. It delivers explainable, real-time diagnostic feedback and automatically converts diagnoses into medical codes to reduce misses, delays, or inaccuracies throughout the process from diagnosis to documentation. DxPrime also supports surgical coding and the detection of left-right laterality mismatches. By identifying diagnostic gaps early and automating diagnostic workflows, it helps physicians to focus more on diagnosing and treating patients while significantly reducing administrative burden. "After participating in the Mayo Clinic Platform_Accelerate program in 2022, we have continued to refine the core model behind DxPrime to enable physicians to make faster, more accurate decisions with fewer distractions," said Jim Long, CEO of AESOP Technology. "In an era of increasingly complex diseases and growing workforce shortages, this mission is more critical than ever. We are proud that DxPrime is now a qualified solution on the Mayo Clinic Platform, ready to support over 52,000 physicians around the world across the Mayo Clinic Care Network in delivering better and safer care."
Mayo Clinic does not endorse or warrant the third-party products or services made available through Mayo Clinic Platform, including their functionality, quality, or performance. Mayo Clinic expressly disclaims any express or implied warranties on such third-party products or services, including any implied warranties of merchantability, quality, accuracy, fitness for a particular purpose, or noninfringement. All use of these third-party products or services, including applicable rights or remedies, is governed by separate terms with the applicable third-party developers or providers. Reference: Yahoo FInance
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 PharmStars Accelerator Spring 2025 Startup Graduates PharmStars, the pharma-focused accelerator for digital health startups, is delighted to announce that 12 startups graduated from its Spring 2025 program focusing on “Digital Innovations in Rare Disease.” The graduating startups completed PharmaU, PharmStars’ 10-week educational and mentoring program. PharmaU culminated recently with a Showcase Event in Boston that brought together participating startups and PharmStars’ innovation-minded pharma members. PharmStars is dedicated to bridging the "pharma-startup gap." The accelerator's mission is to help biopharma firms and digital health startups overcome barriers to partnership due to differences in size, speed, processes, and culture, thereby accelerating the adoption of digital innovations to improve patient outcomes. PharmaU prepares participating startups to effectively engage with pharma companies as clients and partners. The 12 startups were selected in March through a highly competitive application process that attracted applicants from 10 countries. The startups offer unique digital health innovations for rare disease, including diagnostics and biomarkers for patient identification and disease monitoring, tools to improve the execution of rare disease clinical trials, and patient research and advocacy group solutions. The graduating startups were thrilled with their PharmaU education. Jan-Willem Hoste, CEO of Meep and a Spring 2025 PharmStars graduate said, “After putting into practice the mentoring and education we received from PharmStars, we noticed the difference in how our value proposition was received during our sales discussions — we could get to the essence quicker as we spoke their language and understood pharma’s challenges in a deeper way. PharmaU got us there!” Anna Chukaeva, CEO of Intercellular, and another Spring 2025 graduate, concurred, “I'm really thankful for PharmStars. I got more than I ever expected. With most accelerators, the value is signaling: you got accepted into the accelerator and exposure at demo day, but that's it. PharmStars does way more. Through PharmaU, we learned a lot, worked closely with amazing mentors, and started meaningful conversations with potential customers.” At the Showcase Event, startups presented their solutions to PharmStars’ pharma members and then met with them individually. More than 70 private, one-on-one startup/pharma meetings took place over two days. The opportunity to connect with pharmaceutical stakeholders is incredibly impactful, explained Spring 2025 graduate, Amanda Clark, CEO of PulManage, “The fireside chats helped us understand the pharma members. When we got to see them at the Showcase, the groundwork had already been laid so we could start conversations, continue them in the one-on-one meetings, and build relationships.” The 12 digital health startups completing the Spring 2025 PharmStars accelerator are:
PharmStars is now open for applications for its upcoming Fall 2025 cohort focused on “Innovations in Data Management and Insights.” New pharma and biotech members are welcome to join the program. Digital health startups interested in participating can find additional details and the application on PharmStars’ website, www.PharmStars.com. About PharmStars PharmStars is the member-based, pharma-focused accelerator for digital health startups. Because of our expertise across pharma, startups, digital health, and innovation, we understand the challenges that pharma and startups face when seeking to collaborate. Our PharmaU program supports digital health startups and our pharma members in “bridging the pharma-startup gap,” leading to greater success and faster adoption of “beyond the molecule” solutions. More information at www.PharmStars.com.
The growing demand for personalized medicine has accelerated the adoption of Real-World Data (RWD) in healthcare. The Tungs' Taichung MetroHarbor Hospital in Taiwan, in collaboration with AESOP Technology, conducted research on an AI-driven Clinical Decision Support System (CDSS) that leverages RWD to enable safer and more effective clinical decision-making, significantly reducing the risk of potentially inappropriate medications. The findings were published in the Journal of Medical Internet Research recently.
RWD encompasses various data types, including electronic health records (EHR), insurance claims, wearable devices, environmental factors, and social determinants of health. It offers a comprehensive view of patient conditions and treatment outcomes. However, effectively utilizing RWD remains a significant challenge. As a key component of RWD, the EHR system is often constrained by the poor design of traditional CDSS. These systems frequently fire irrelevant or low-priority alerts and fail to provide specific recommendations for complex scenarios, such as off-label drug use, multimorbidity, and polypharmacy. This results in alert fatigue among physicians, causing critical alerts and reminders to be overlooked. Consequently, the completeness and accuracy of medical records are compromised, increasing the risk of inappropriate diagnoses or treatments and potentially threatening patient safety. This study addresses these challenges with an integrated AI-powered CDSS that combines MedGuard (now called RxPrime) for prescription appropriateness and DxPrime for diagnostic recommendations. By analyzing 438,558 prescriptions during a year-long trial, the system delivered 10,006 actionable recommendations, achieving a nearly 60% acceptance rate by physicians. Compared to traditional systems, this AI-enhanced approach demonstrated superior precision and practical applicability in real-world clinical settings. The results also revealed high acceptance rates in specialties such as ophthalmology (96.59%) and obstetrics/gynecology (90.01%), indicating strong applicability. In contrast, lower acceptance rates in neurology (38.54%) and hematology-oncology (10.94%) underscore the need for specialty-specific customization to address diverse clinical demands. This research highlights the transformative potential of RWD-driven AI systems with actionable recommendations to improve patient safety and support complex treatment decisions. These advancements foster greater trust and adoption of CDSS by physicians. Furthermore, by enhancing the completeness and accuracy of medical records, these systems elevate the quality of RWD, fostering a positive feedback loop that drives future medical advancements and consistently provides a reliable foundation for data-driven healthcare. AESOP Technology Unveils World's First Machine Learning Model to Combat Wrong-Site Surgery2/18/2025
Wrong-site surgery (WSS), a critical "Never Event," represents a failure that should never occur in healthcare. Yet, due to underreporting, the true prevalence of these incidents remains obscured, jeopardizing patient safety and healthcare management. AESOP Technology, a medical AI startup, has developed an innovative solution: the Association Outlier Pattern (AOP) machine learning model. This model offers real-time decision support and retrospective analysis, aimed at enhancing surgical safety and care quality. According to the World Health Organization's (WHO) 2024 Patient Safety Report, a mere 38% of countries have established reporting systems for never events. In the United States, the Joint Commission documented 112 surgical errors in 2023, with wrong-site surgeries comprising 62% of these incidents. The absence of comprehensive reporting hinders the healthcare system's ability to gauge the issue's magnitude and implement effective preventative measures. Inconsistent documentation is one of the major contributors to WSS. To address this, AESOP utilized data from the Centers for Medicare & Medicaid Services Limited Data Set (2017–2020), examining discrepancies in surgical laterality. This analysis informed the creation of the AOP model—the first of its kind dedicated to addressing WSS. Unlike traditional rule-based systems that merely verify consistency, the AOP model analyzes intricate patterns between diagnoses and surgeries. It excels in handling incomplete or ambiguous diagnostic data, achieving an accuracy rate of over 80% in identifying surgical errors, outperforming existing methods. The AOP model empowers healthcare organizations to detect inconsistencies in medical records, identify unreported surgical errors, and enhance reporting mechanisms. This not only improves patient safety but also strengthens management systems for error prevention. Beyond retrospective analysis, the AOP model offers real-time decision support during surgical planning. It automatically flags incorrect associations between surgical codes and diagnoses, ensuring accurate and complete records. This real-time capability reduces error risks, making the AOP model an essential tool for future electronic health record (EHR) systems. "We are thrilled with the preliminary outcomes of our research and look forward to integrating these insights into DxPrime's patient safety features this year," said Jim Long, CEO of AESOP Technology. "Our advancements in automating surgery coding show great potential for helping physicians deliver safer care, reduce documentation time, and enable medical coders to perform better concurrent surgery coding and review when patients are still hospitalized." Having demonstrated its efficacy in orthopedics, the AOP model holds promise for other specialties reliant on laterality, such as ophthalmology and otolaryngology. This expansion aligns with AESOP's commitment to advancing patient-centered AI solutions across diagnostics, medication safety, and now surgical safety—ushering in a new era of reliable and safer healthcare.
 醫守科技「RxPrime藥御守」畫面：系統分析此病人的病歷資料，確認沒有相對應的診斷或證據可以解釋使用ACETAZOLAMIDE的臨床合理性，建議刪除或加入對應此藥的診斷。醫守科技／提供 疾病的種類越來越複雜，相對治療各種疾病、併發症的藥物處方箋亦然，然而醫生面對病人的看診時間並未增加，反而越來越短，從北醫衍生獨立的醫守科技，今年獲選中小企業暨新創署「黑科技」類的潛力新創，結合AI技術針對處方箋的診斷與用藥，透過大數據深度學習分析，獨家開發「臨床診斷推論網路模型」，幫助醫師執行臨床用藥決策，強化病人安全。 開錯藥物的新聞時有所聞，醫守科技創辦人暨執行長龍安靖以退燒止痛的普拿疼ACETAMINOPHEN為例，在台灣一年就有3000次開錯，不小心開成青光眼的用藥ACETAZOLAMIDE，兩種藥名長得很像，導致非常容易開錯處方箋。 醫守科技的臨床推論網路模型將每種藥對應的症狀、疾病連結對應，專司防守用藥錯誤的「RxPrime藥御守」如果發現處方箋裡有不合理的用藥，或少開了藥，系統就會提醒建議，等於是請AI幫忙醫生開處方箋時加一道防線。 醫守科技透過蒐集大量數據加上AI的深度學習分析推論，最早從收集台灣健保資料，後來加上美國的資料，目前累積超過32億筆的病歷數據，且不斷在更新中，幾乎涵蓋各科所有疾病；龍安靖說，醫療團隊與AI團隊密集定期會議討論，打磨更新商品的資訊功能；目前競爭對手有各有不同方法，例如美國是找很多專家從上建立指導策略，人做出的知識，說明描述比較清楚，但很難做到全科系完整的覆蓋。 除了用藥識別，相同技術也多元彈性運用在解決醫療、用藥不同痛點，例如健保申請，經常容易出現的漏帳或超收，透過AI推論網路模型可以更有效率校正；此外藥廠有些新藥推廣不易，將資訊放在平台，可以讓新藥直接與第一線看診醫師接觸。新藥只有十年專利，但前3、4年都在研發、推廣，過了專利期學名藥就出來了，對於藥廠在市場推廣新藥是一大利器。 近幾年智慧醫療研發創新名目眾多，醫守是少數在智慧醫療領域成功出海的新創公司，且團隊第一筆資金就來自美國矽谷，團隊提出的解決方案受到肯定，在台灣醫守的產品已有將近一半醫學中心採用，也成功打入美國市場，龍安靖表示，醫界的改革通常很慢、很謹慎，醫守科技提出的並非顛覆性改革，而是站在幫助醫療人員的立場，解決現有的痛點，醫療的挑戰很多，這也是醫守科技的使命與機會。 Reference:
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