John McMenamy | Health Professions | Best Researcher Award

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Dr. John McMenamy | Health Professions | Best Researcher Award

Dr. John McMenamy, Denver Health & Hospital Authority, United States

Dr. John McMenamy, MD, MBA, CPE, CMQ, is a distinguished radiologist specializing in neuroradiology and diagnostic imaging. Born in St. Louis, Missouri, he has over two decades of experience in medical imaging and healthcare leadership. Currently serving as Associate Professor at the University of Colorado School of Medicine and Associate Chair of Radiology at Denver Health, Dr. McMenamy has been instrumental in advancing radiological practices and integrating innovative technologies to enhance patient care. His commitment to excellence is evident in his efforts to streamline clinical operations, implement AI-driven imaging solutions, and foster a culture of continuous improvement within healthcare systems. Beyond his clinical duties, he is recognized for his contributions to medical education and quality improvement initiatives, making significant strides in optimizing healthcare delivery and patient outcomes.

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John McMenamy, MD, MBA: Suitability for the Research for Best Researcher Award

Dr. John McMenamy, MD, MBA, stands out as an exceptional candidate for the Research for Best Researcher Award due to his comprehensive contributions to clinical radiology, operational efficiency, and leadership in healthcare innovation. His educational background, consisting of an MD from the University of Texas Medical School and an MBA from the University of Massachusetts, Amherst, coupled with specialized postdoctoral training, provides a solid foundation for his leadership in radiology. With certifications in radiology and clinical quality, Dr. McMenamy has proven himself as a subject matter expert in medical imaging and healthcare management.

His leadership roles, particularly as the Associate Chair of the Department of Radiology at Denver Health, demonstrate a track record of advancing clinical operations, quality, and safety improvements. Dr. McMenamy has significantly contributed to the optimization of imaging workflows, including the implementation of artificial intelligence (AI) platforms and dual-energy CT (DECT) to improve diagnostic accuracy and patient outcomes. His strategic vision for Denver Health's Radiology Department, developed collaboratively with colleagues, has had a profound impact on patient experience, clinical efficiency, and cost-saving measures. For instance, his initiative to implement an AI-assisted workflow increased CT capacity by 36%, while improving radiologist productivity and turnaround times by over 20%.

Education 

Dr. McMenamy's academic journey began with dual Bachelor of Science and Bachelor of Arts degrees in Biology from Truman State University in 2000. He earned his Doctor of Medicine from the University of Texas Medical School at Houston in 2004. Following this, he completed an internal medicine internship at SSM St. Mary's Health Center in St. Louis. He pursued a residency in Radiology and a fellowship in Neuroradiology at the University of Texas Southwestern Medical Center, concluding in 2010. To augment his clinical expertise with administrative acumen, Dr. McMenamy obtained an MBA from the University of Massachusetts, Amherst, in 2017. His commitment to quality improvement is further demonstrated by his participation in the Clinical Quality Fellowship Program through the Greater New York Hospital Association/United Hospital Fund. He holds certifications from the American Board of Radiology in Diagnostic Radiology and Neuroradiology, as well as credentials in medical quality and physician executive leadership.​

Professional Experience 

Dr. John McMenamy's professional career is marked by leadership roles and clinical excellence. At Denver Health, he serves as Associate Chair of Radiology, where he oversees clinical operations, IT integration, and quality initiatives. His tenure includes the development of strategic visions and operational frameworks that have enhanced departmental efficiency and patient care. Dr. McMenamy has been pivotal in implementing AI-assisted diagnostic tools, optimizing imaging protocols, and improving emergency department workflows. His previous roles include faculty appointments at NYU School of Medicine, where he contributed to academic and clinical advancements in radiology. His expertise extends to disaster preparedness and mass casualty incident response, reflecting a comprehensive approach to healthcare delivery. Through collaborative efforts with multidisciplinary teams, he has successfully led initiatives that reduced unnecessary imaging, improved turnaround times, and enhanced overall patient experience. His career exemplifies a commitment to innovation, education, and quality in the medical field.​

Awards and Recognition 

Dr. McMenamy's contributions to medicine have earned him numerous accolades. He is a Certified Physician Executive (CPE) and holds the Certified in Medical Quality (CMQ) designation, reflecting his dedication to healthcare excellence. His leadership in radiology has been recognized through appointments to key positions at Denver Health and the University of Colorado School of Medicine. He has been instrumental in implementing high-reliability organizational practices, leading to improved trauma imaging performance and patient outcomes. His work in standardizing inpatient MRI workflows has been highlighted by the Greater New York Hospital Association, showcasing his impact on healthcare quality and efficiency. Dr. McMenamy's research and publications in peer-reviewed journals further underscore his commitment to advancing medical knowledge and practice. His peers and institutions have consistently acknowledged his innovative approaches and dedication to improving healthcare systems.​

Research Skills On Health Professions

Dr. McMenamy possesses extensive research expertise in radiology, with a focus on neuroradiology, trauma imaging, and the integration of artificial intelligence in clinical workflows. His scholarly work includes studies on imaging techniques for necrotizing fasciitis, facial fractures, and the application of AI to enhance emergency department CT capacity. He has contributed to the development of high-reliability organizational approaches to improve trauma imaging performance, emphasizing the importance of culture and teamwork in healthcare settings. His research on the performance of noncontrast CT stroke platforms demonstrates his commitment to advancing diagnostic accuracy and patient care. Dr. McMenamy's ability to translate complex research findings into practical applications has significantly impacted clinical protocols and patient outcomes. His collaborative efforts with multidisciplinary teams highlight his skill in integrating research into everyday medical practice, ensuring that innovations directly benefit patients and healthcare systems.

 Publication Top Notes

  • Electronic Health Record Improvements to Reduce Emergency Department CT Prescan Times at a Safety Net Hospital

    • Authors: Hardik Patel, Mouna Chebaane, Rolando G. Gerena, Corey A. Thompson, Adam Schwertner, Bradley D. Shy, David M. Naeger, John McMenamy

    • Journal: Journal of the American College of Radiology

    • Year: 2025

  • A “High-Reliability Organization” Approach to Improve Trauma Imaging Performance

    • Authors: John McMenamy, Ahmad Garada, Sergey Kochkine, Randy Miles, David M. Naeger

    • Journal: Journal of the American College of Radiology

    • Year: 2023

  • Brainstorming Our Way to Improved Quality, Safety, and Resident Wellness in a Resource-Limited Emergency Department

    • Authors: Luke A. Ginocchio, John Rogener, Ryan Chung, Xi Xue, Dean Tarnovsky, John McMenamy

    • Journal: Current Problems in Diagnostic Radiology

    • Year: 2022

  • Assistance from Automated Aspects Software Improves Reader Performance

    • Authors: Not listed

    • Journal: Journal of Stroke & Cerebrovascular Diseases

    • Year: 2021

  • A Resident-driven Intervention to Decrease Door-to-needle Time and Increase Resident Satisfaction in a Resource-limited Setting

    • Authors: Not listed

    • Journal: Neurology

    • Year: 2018

  • A Resident-driven Intervention to Decrease Door-to-needle Time and Increase Resident Satisfaction in a Resource-limited Setting

    • Authors: Not listed

    • Journal: Stroke

    • Year: 2018

  • Use of a Referring Physician Survey to Direct and Evaluate Department-Wide Radiology Quality Improvement Efforts

    • Authors: Not listed

    • Journal: Journal of the American College of Radiology

    • Year: 2015

  • MDCT Diagnosis of Acute Pulmonary Embolism in the Emergent Setting

    • Authors: Not listed

    • Journal: Emergency Radiology

    • Year: 2015

  • Differentiating Shunt-Responsive Normal Pressure Hydrocephalus from Alzheimer Disease and Normal Aging: Pilot Study Using Automated MRI Brain Tissue Segmentation

    • Authors: Not listed

    • Journal: Journal of Neurology

    • Year: 2014

  • MRI of the Petromastoid Canal in Children

    • Authors: Not listed

    • Journal: Journal of Magnetic Resonance Imaging

    • Year: 2014

 

Le Yao | Computer Science | Best Researcher Award

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Prof. Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao, Hangzhou Normal University, China

Le Yao is an accomplished Associate Professor at the School of Mathematics, Hangzhou Normal University, China. With a strong background in control science and engineering, he specializes in data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. His research focuses on deep learning, interpretable modeling, and causal analysis for industrial applications. Le Yao has been actively involved in multiple funded projects supported by NSFC and the China Postdoctoral Science Foundation. He has an impressive academic record, with numerous high-impact publications in IEEE Transactions and other renowned journals. Recognized for his contributions, he has received prestigious awards, including the National Scholarship for Ph.D. and Outstanding Dissertation Awards. His innovative work bridges the gap between theoretical advancements and practical applications in industrial processes, making significant contributions to smart manufacturing and intelligent systems.

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Summary of Suitability for the ‘Research for Best Researcher Award’

Le Yao is an exceptional candidate for the ‘Research for Best Researcher Award,’ given his impressive academic journey, extensive research contributions, and leadership in the field of industrial data-driven modeling. His work focuses on crucial areas such as soft sensor modeling, quality prediction, fault diagnosis, and causal analysis, with significant contributions to process control in industrial settings. His innovations in deep learning, causal analysis, and interpretable process modeling have greatly advanced the application of machine learning techniques to complex, large-scale industrial systems.

Notably, his research on scalable and distributed parallel modeling for big process data, combined with his exploration of probabilistic modeling and causal discovery methods, reflects a profound understanding of both theoretical and practical aspects of industrial systems. His ability to fuse domain knowledge with data-driven techniques has led to breakthroughs in process quality prediction and fault detection, impacting industries significantly. Furthermore, Le Yao has successfully secured competitive research funding from prestigious sources, such as the National Natural Science Foundation of China (NSFC) and the China Postdoctoral Science Foundation, demonstrating his capability to lead high-level research initiatives.

🎓 Education

Le Yao holds a Ph.D. in Control Science and Engineering from Zhejiang University (2019), where he specialized in big process data modeling, quality prediction, and process monitoring. His doctoral studies were pivotal in advancing soft sensor modeling techniques for industrial applications. Prior to his Ph.D., he earned an M.S. (2015) from Jiangnan University, where he focused on soft sensor modeling and system identification. His bachelor’s degree (2012) was also from Jiangnan University, where he developed a strong foundation in control science and engineering. Throughout his academic journey, Le Yao has consistently demonstrated excellence, securing prestigious scholarships and honors. His multidisciplinary expertise enables him to develop innovative solutions for industrial automation, smart manufacturing, and data-driven decision-making. His research contributions have influenced numerous industrial applications, bridging the gap between academic advancements and real-world implementations.

💼 Professional Experience 

Le Yao is currently an Associate Professor at Hangzhou Normal University (2022–present), where he leads research on deep learning, causal analysis, and interpretable modeling for industrial systems. Prior to this, he served as a Postdoctoral Researcher (2019–2022) at Zhejiang University’s Institute of Industrial Process Control, focusing on deep learning-driven process modeling and process knowledge fusion. During his postdoctoral tenure, he was awarded research grants from NSFC and the China Postdoctoral Science Foundation. His expertise spans scalable and distributed parallel modeling, soft sensor applications, and quality prediction in large-scale industrial systems. Le Yao’s research integrates advanced computational techniques with practical industrial challenges, driving innovation in smart manufacturing. His leadership in industrial data analytics and AI-driven process control has positioned him as a key contributor to the field, influencing both academic research and industry practices.

🏅 Awards and Recognition

Le Yao has been recognized with numerous prestigious awards for his academic and research contributions. He received the 2020 Outstanding Dissertation Award from the Chinese Institute of Electronics and was named an Outstanding Graduate by Zhejiang University and Zhejiang Province in 2019. His research excellence has been acknowledged through multiple National Scholarships for Ph.D. students (2017, 2018). His work has been featured in top-tier conferences, earning him Best Paper Finalist awards at IEEE DDCLS (2018) and China Process Control Conferences (2016, 2017, 2018). These accolades reflect his outstanding contributions to industrial process modeling, soft sensing, and causal analysis. His innovative approaches to quality prediction and fault diagnosis have significantly impacted the field, earning him recognition from both academic institutions and industry leaders. Le Yao’s commitment to excellence continues to drive his research endeavors, making him a prominent figure in data-driven industrial applications.

🌍 Research Skills On Computer Science

Le Yao’s research expertise spans multiple domains, including data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. He specializes in deep learning techniques for process optimization and interpretable modeling to enhance decision-making in industrial environments. His work on scalable and distributed parallel modeling has introduced novel methodologies for handling big process data efficiently. His causal analysis research integrates process knowledge with data-driven approaches, improving anomaly detection and fault diagnosis. He has developed advanced deep learning models incorporating hierarchical extreme learning machines and probabilistic latent variable regression. His research contributions have been implemented in real-world industrial applications, optimizing quality prediction and process control. With a strong foundation in control engineering, statistics, and artificial intelligence, Le Yao continues to advance the field by bridging theoretical research with industrial needs.

📖 Publication Top Notes

  • Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application

    • Authors: L Yao, Z Ge
    • Citation: 295
    • Year: 2017
    • Journal: IEEE Transactions on Industrial Electronics, 65 (2), 1490-1498

  • Big data quality prediction in the process industry: A distributed parallel modeling framework

    • Authors: L Yao, Z Ge
    • Citation: 108
    • Year: 2018
    • Journal: Journal of Process Control, 68, 1-13

  • Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure

    • Authors: B Shen, L Yao, Z Ge
    • Citation: 102
    • Year: 2020
    • Journal: Control Engineering Practice, 94, 104198

  • Scalable semisupervised GMM for big data quality prediction in multimode processes

    • Authors: L Yao, Z Ge
    • Citation: 90
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (5), 3681-3692

  • Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data

    • Authors: L Yao, Z Ge
    • Citation: 80
    • Year: 2016
    • Journal: IEEE Transactions on Automation Science and Engineering, 14 (1), 126-138

  • Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2019
    • Journal: Engineering Applications of Artificial Intelligence, 81, 450-465

  • Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2017
    • Journal: Control Engineering Practice, 61, 72-80

  • Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction

    • Authors: L Yao, Z Ge
    • Citation: 61
    • Year: 2020
    • Journal: IEEE Transactions on Industrial Informatics, 17 (6), 3782-3792

  • Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach

    • Authors: L Yao, Z Ge
    • Citation: 60
    • Year: 2017
    • Journal: IEEE Transactions on Instrumentation and Measurement, 66 (8), 1985-1993

  • Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data

    • Authors: W Shao, L Yao, Z Ge, Z Song
    • Citation: 53
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (8), 6362-6373

WEI-CHENG LIEN | Artificial Intelligence | Best Researcher Award

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Dr. WEI-CHENG LIEN | Artificial Intelligence | Best Researcher Award

👤 Dr. WEI-CHENG LIEN, Hyson Technology Inc, Taiwan

Wei-Cheng Lien is the CEO and Co-Founder of HysonTech Inc., based in Taiwan, where he leads the company’s AIoT solutions for various industries. With a Ph.D. in Electrical Engineering from National Cheng Kung University, Lien specializes in AI, AIoT hardware/software integration, and digital transformation. He has held significant leadership roles across diverse organizations, including his work as a director for the Taiwan Artificial Intelligence Association and as an industry technology advisor for the National Applied Research Laboratories. Lien’s contributions to fields such as medical diagnostics, smart traffic enforcement, and manufacturing automation have earned him recognition globally. His work has influenced both academia and industry, with numerous awards and patents to his name. He continues to innovate at the intersection of AI and IoT, driving digital transformation and leading key projects like Taiwan Taoyuan International Airport Terminal 3.

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🌟 Research for Best Researcher Award: Wei-Cheng Lien

Suitability for the Award:

Wei-Cheng Lien is exceptionally suited for the Research for Best Researcher Award due to his remarkable achievements in both the academic and entrepreneurial realms, coupled with his extensive experience in the field of electrical engineering and artificial intelligence (AI). With a Ph.D. in Electrical Engineering (EE) from National Cheng Kung University and a track record of over 50 technical papers and 9 patents, Dr. Lien has made significant contributions to various cutting-edge areas, such as AI, AIoT (Artificial Intelligence of Things) hardware/software integration, and digital transformation.

As the CEO of HysonTech Inc., he has been instrumental in driving innovative AIoT solutions across diverse industries, from aquaculture to medical diagnostics, emphasizing his capability to translate research into impactful real-world applications. His leadership extends beyond his company, as he holds prominent advisory roles with leading institutions like the Taiwan National Science and Technology Council and the Institute for Information Industry. His leadership in AI and technology is further demonstrated by his contributions to the Taiwan Artificial Intelligence Association and several high-profile projects such as the Taiwan Taoyuan International Airport Terminal 3 development.

🎓 Education 

Wei-Cheng Lien earned his Ph.D. in Electrical Engineering (EE) from National Cheng Kung University (NCKU), Taiwan, where he achieved a remarkable GPA of 4.0. His educational journey also includes a Bachelor’s degree in EE from NCKU, where he earned a GPA of 3.4. During his academic tenure, Lien demonstrated a strong focus on Artificial Intelligence, AIoT, and digital transformation, with his research laying the groundwork for his subsequent professional achievements. Lien’s time at Duke University as a visiting scholar further broadened his perspective, and his contributions to AI and electronics research gained significant recognition. His education, coupled with extensive hands-on experience in AI and hardware/software integration, has shaped him into a leader in the industry. Lien’s academic excellence and innovation are the bedrock upon which his diverse professional experiences and entrepreneurial ventures are built.

💼  Professional Experience

Wei-Cheng Lien is the CEO and Co-Founder of HysonTech Inc., where he leads innovative AIoT solutions across sectors like aquaculture, smart traffic enforcement, and medical diagnostics. Under his leadership, HysonTech integrates AI technologies into hardware and software systems, offering complete solutions to its clients. Prior to this, Lien served as the CTO of Slash Living Culture Company, where he developed reed-based plant straws and building materials, incorporating AIoT monitoring techniques. His career also includes his role as Manager of the Intelligence System Division at AVITONE CO., LTD., where he focused on image system integration for advanced X-ray scanners. Lien’s earlier experience at MediaTek Inc. as Senior Engineer and Fellow Student further honed his expertise in AI and computing technologies. His extensive professional experience spans AI integration, digital transformation, and innovative product development, driving growth and technological advancement in multiple industries.

🏅Awards and Recognition

Wei-Cheng Lien has been honored with over 60 prestigious awards throughout his career. Notably, he was named the 2025 Outstanding Alumni of Taichung Municipal Taichung First Senior High School and received the 2024 Taiwan AI Award at the AI Taiwan x Future Commerce Exhibition. Lien’s achievements in AI and digital transformation were recognized with the 2024 Taiwan InnoTech Expo’s Two Invention Medals, along with Best Paper Awards at various conferences, including the Investigative Technology and Forensic Science Symposium. He was also awarded the 2024 Best Conference Paper Award at the IEEE International Conference on Electronic Communications, Internet of Things, and Big Data. Lien’s entrepreneurial success was acknowledged with the 2024 Best Startup Selection in Beijing and other notable innovation awards. Additionally, he was recognized with the 2023 EE Times Asia Award for Best AI Product of the Year and numerous awards from government and industry organizations for his contributions to AI and digital transformation.

🌍 Research Skills Artificial Intelligence

Wei-Cheng Lien’s research skills lie at the forefront of AI, AIoT integration, and digital transformation. His expertise spans IC design, testing, diagnosis, and software-hardware systems integration, which allows him to create innovative solutions across a range of industries. Lien’s work in AI computing, particularly in CPU DFT architectures and low-cost test techniques for System on Chips (SoCs), has been instrumental in reducing test costs and improving design accuracy. His research on output selection for SOC debug systems has earned global recognition for reducing test response volumes and minimizing area overhead. Lien has authored over 50 technical papers and holds 9 patents, further demonstrating his proficiency in cutting-edge research. His ability to bridge the gap between theoretical knowledge and practical application has been a key factor in his contributions to AI-driven industries such as healthcare, smart cities, and advanced manufacturing. Lien’s ongoing research continues to push the boundaries of AI and IoT technology.

📖 Publication Top Notes

  • A rapid household mite detection and classification technology based on artificial intelligence-enhanced scanned images
    • Authors: Lin, L.H.-M., Lien, W.-C., Cheng, C.Y.-T., Lai, Y.-T., Peng, Y.-T.
    • Citation: Internet of Things (The Netherlands), 2025, 29, 101484
  • Image Demoiréing via Multiscale Fusion Networks With Moiré Data Augmentation
    • Authors: Peng, Y.-T., Hou, C.-H., Lee, Y.-C., Lin, Y.-T., Lien, W.-C.
    • Citation: IEEE Sensors Journal, 2024, 24(12), pp. 20114–20127
    • Citations: 3
  • Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods
    • Authors: Lai, Y.-T., Peng, Y.-T., Lien, W.-C., Liao, C.-J., Chiu, Y.-S.
    • Citation: Aquaculture, 2024, 586, 740741
    • Citations: 2
  • Traffic Violation Detection via Depth and Gradient Angle Change
    • Authors: Peng, Y.-T., Liu, C.-Y., Liao, H.-H., Lien, W.-C., Hsu, G.-S.J.
    • Citation: 2022 IEEE 7th International Conference on Intelligent Transportation Engineering, ICITE 2022, 2022, pp. 326–330
    • Citations: 1
  • Unveiling of How Image Restoration Contributes to Underwater Object Detection
    • Authors: Peng, W.-Y., Peng, Y.-T., Lien, W.-C., Chen, C.-S.
    • Citation: 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021, 2021
    • Citations: 7
  • Output bit selection methodology for test response compaction
    • Authors: Lien, W.-C., Lee, K.-J.
    • Citation: Proceedings – International Test Conference, 2016, 0, 7805873
  • A Test-per-cycle BIST architecture with low area overhead and no storage requirement
    • Authors: Shiao, C.-M., Lien, W.-C., Lee, K.-J.
    • Citation: 2016 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2016, 2016, 7482556
    • Citations: 11
  • Efficient LFSR Reseeding Based on Internal-Response Feedback
    • Authors: Lien, W.-C., Lee, K.-J., Hsieh, T.-Y., Chakrabarty, K.
    • Citation: Journal of Electronic Testing: Theory and Applications (JETTA), 2014, 30(6), pp. 673–685
    • Citations: 7
  • Output-bit selection with X-avoidance using multiple counters for test-response compaction
    • Authors: Lien, W.-C., Lee, K.-J., Chakrabarty, K., Hsieh, T.-Y.
    • Citation: Proceedings – 2014 19th IEEE European Test Symposium, ETS 2014, 2014, 6847823
    • Citations: 3
  • Output selection for test response compaction based on multiple counters
    • Authors: Lien, W.-C., Lee, K.-J., Chakrabarty, K., Hsieh, T.-Y.
    • Citation: Technical Papers of 2014 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2014, 2014, 6834865
    • Citations: 2

 

FANCY GAETE | Medicine | Women Researcher Award

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Dr. FANCY GAETE | Medicine | Women Researcher Award

👤 Dr. FANCY GAETE, HOSPITAL SANTIAGO ORIENTE DR. LUIS TISNÉ BROUSSE, Chile

Dr. Fancy Gaete Verdejo is a renowned anatomopathologist with 26 years of experience in Chile’s public healthcare system. As a pioneer in breast cancer diagnostics, she established the national reference center for the in situ hybridization technique for HER2 testing, which has been operational since 2011. In 2022, she transitioned from the manual FISH HER2 technique to the automated DISH HER2 method, integrating digital pathology and AI algorithms. This innovation led to a 13.02% reduction in costs, an 83.86% improvement in response time, and an 11.29% increase in productivity. Dr. Gaete is actively involved in the advancement of pathology practices and plays a leadership role as the President of the Chilean Society of Pathological Anatomy and the Chilean Society of Mastology.

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🌟  Assessment of Suitability for the Research for Women Researcher Award

Summary of Suitability
Dr. Fancy Gaete Verdejo exemplifies an exceptional commitment to advancing research and innovation in pathology, specifically breast cancer diagnostics, within the Chilean public health system. Her pioneering efforts to centralize the HER2 in situ hybridization technique have significantly impacted breast cancer pathology in Chile. By introducing the automated DISH HER2 technique integrated with digital pathology and artificial intelligence (AI) algorithms, she has revolutionized diagnostic efficiency, reducing costs by 13.02%, cutting response times by 83.86%, and improving productivity by 11.29%.

Dr. Verdejo’s leadership in developing a national reference center for HER2 analysis has not only elevated the quality of public healthcare but also fostered a data-driven approach to diagnostics. Her dedication to reducing healthcare disparities through affordable and accessible diagnostic innovations underscores her suitability for this award. Moreover, her current roles as President of the Chilean Society of Pathological Anatomy and the Chilean Society of Mastology further highlight her influence and commitment to the field.

🎓  Education

Dr. Gaete completed her medical studies at the University of Chile, where she specialized in Anatomopathology. Her passion for advancing medical diagnostics led her to focus on molecular pathology, particularly in breast cancer. She further honed her skills by obtaining advanced training in digital pathology and artificial intelligence (AI) techniques, ensuring she stayed at the forefront of technological innovations in the field. Throughout her career, Dr. Gaete has pursued continuous education, contributing significantly to the implementation of cutting-edge technologies in public health systems. Her work has not only reshaped breast cancer diagnostic processes but has also inspired others in the field to incorporate AI and digital tools to improve healthcare outcomes.

💼  Professional Experience 

Dr. Fancy Gaete Verdejo has dedicated over 26 years of service in the Chilean public healthcare system. As an anatomopathologist, she has specialized in the diagnosis of breast cancer, implementing groundbreaking techniques and driving efficiency in pathology labs. In 2011, she established the national reference center for HER2 in situ hybridization in breast cancer, advancing diagnostic capabilities. Dr. Gaete’s work became more impactful in 2022 when she led the transition from manual FISH HER2 testing to an automated DISH HER2 method, integrating digital pathology with AI algorithms. This transition dramatically improved the turnaround time, reduced costs, and enhanced productivity within the system. Beyond her technical expertise, Dr. Gaete holds leadership roles in major Chilean medical societies, such as the Chilean Society of Pathological Anatomy and the Chilean Society of Mastology, where she contributes to the shaping of national health policies.

🏅  Awards and Recognition

Dr. Gaete’s pioneering contributions have earned her numerous accolades throughout her distinguished career. She has been recognized for her role in advancing breast cancer diagnostics in Chile, particularly through her development of the national reference center for HER2 in situ hybridization. Her innovative work in integrating digital pathology with AI has also been widely acknowledged for improving diagnostic accuracy and system efficiency. Additionally, Dr. Gaete’s leadership in the Chilean Society of Pathological Anatomy (President 2023-2026) and the Chilean Society of Mastology (President 2024-2026) reflects the esteem in which she is held by her peers. These positions have enabled her to influence national healthcare policies and standards in pathology and oncology. Her work has also led to several collaborations with international institutions, further expanding her impact on the global healthcare community.

🌍  Research Skills On Medicine

Dr. Gaete is an expert in anatomical pathology with a deep focus on molecular and digital pathology, particularly in breast cancer. Her research skills include a solid understanding of diagnostic techniques, data analysis, and the integration of innovative technologies such as artificial intelligence (AI) and digital imaging into pathology workflows. She excels in evaluating the economic impact of medical technologies, particularly in healthcare systems. Her research has significantly influenced the adoption of digital pathology and AI in Chile, demonstrating how these tools can optimize diagnostic precision and efficiency. Furthermore, Dr. Gaete is highly skilled in research management, leading both academic and industry collaborations. Her work has contributed to the publication of influential studies on the cost-effectiveness of healthcare technologies, such as her ongoing research on the economic impact of integrative digital pathology in public breast cancer diagnostics.

📖 Publication Top Notes

  • Impact of the Chilean explicit guarantees health system (GES) on breast cancer treatment | Resultados del tratamiento del cáncer de mama, programa nacional de cáncer del adulto
    • Authors: Castillo, C.S.M., Cabrera, M.E.C., Derio P., L., Gaete V., F., Cavada CH., G.
    • Journal: Revista Medica de Chile
    • Year: 2017
    • Volume: 145(12)
    • Pages: 1507–1513
    • Citations: 14
  • Spontaneous quadruplet pregnancy. A challenge for the multidisciplinary team in a health service | Gestación cuádruple espontánea. Un desafío para el equipo multidisciplinario en un servicio de salud
    • Authors: Salgado M., E., Lattus O., J., Barrera C., V., Fritis L., A., Gaete V., F.
    • Journal: Revista Chilena de Obstetricia y Ginecologia
    • Year: 2006
    • Volume: 71(1)
    • Pages: 35–42
    • Citations: 1
  • Jejunal diverticulosis as cause of gastrointestinal hemorrhages. Case report | Diverticulosis yeyunal: Una causa infrecuente de hemorragia digestiva. Caso clínico
    • Authors: Zapata L., R., Rojas S., C., Gaete V., F.
    • Journal: Revista Medica de Chile
    • Year: 2000
    • Volume: 128(10)
    • Pages: 1133–1138
    • Citations: 4