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  1. Article ; Online: Machine Learning for Sepsis Prediction: Prospects and Challenges.

    Yang, He S

    Clinical chemistry

    2024  Volume 70, Issue 3, Page(s) 465–467

    MeSH term(s) Humans ; Sepsis/diagnosis ; Algorithms ; Machine Learning
    Language English
    Publishing date 2024-03-01
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1093/clinchem/hvae006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Robust antibody response in children to acute COVID-19 infection and lasts for months.

    Zhao, Zhen / Yang, He S

    The Journal of pediatrics

    2021  Volume 240, Page(s) 310–313

    MeSH term(s) Antibodies, Viral ; Antibody Formation ; COVID-19 ; Child ; Humans ; Immunoglobulin G ; SARS-CoV-2
    Chemical Substances Antibodies, Viral ; Immunoglobulin G
    Language English
    Publishing date 2021-12-24
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 3102-1
    ISSN 1097-6833 ; 0022-3476
    ISSN (online) 1097-6833
    ISSN 0022-3476
    DOI 10.1016/j.jpeds.2021.10.040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Sheng Yang He.

    Yang He, Sheng

    Current biology : CB

    2018  Volume 28, Issue 7, Page(s) R295–R296

    Abstract: Interview with Sheng Yang He, who studies the molecular biology of plant-pathogen interactions at Michigan State University. ...

    Abstract Interview with Sheng Yang He, who studies the molecular biology of plant-pathogen interactions at Michigan State University.
    Language English
    Publishing date 2018-04-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 1071731-6
    ISSN 1879-0445 ; 0960-9822
    ISSN (online) 1879-0445
    ISSN 0960-9822
    DOI 10.1016/j.cub.2018.02.049
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  4. Article ; Online: Capacity for large language model chatbots to aid in orthopedic management, research, and patient queries.

    Sosa, Branden R / Cung, Michelle / Suhardi, Vincentius J / Morse, Kyle / Thomson, Andrew / Yang, He S / Iyer, Sravisht / Greenblatt, Matthew B

    Journal of orthopaedic research : official publication of the Orthopaedic Research Society

    2024  Volume 42, Issue 6, Page(s) 1276–1282

    Abstract: Large language model (LLM) chatbots possess a remarkable capacity to synthesize complex information into concise, digestible summaries across a wide range of orthopedic subject matter. As LLM chatbots become widely available they will serve as a powerful, ...

    Abstract Large language model (LLM) chatbots possess a remarkable capacity to synthesize complex information into concise, digestible summaries across a wide range of orthopedic subject matter. As LLM chatbots become widely available they will serve as a powerful, accessible resource that patients, clinicians, and researchers may reference to obtain information about orthopedic science and clinical management. Here, we examined the performance of three well-known and easily accessible chatbots-ChatGPT, Bard, and Bing AI-in responding to inquiries relating to clinical management and orthopedic concepts. Although all three chatbots were found to be capable of generating relevant responses, ChatGPT outperformed Bard and BingAI in each category due to its ability to provide accurate and complete responses to orthopedic queries. Despite their promising applications in clinical management, shortcomings observed included incomplete responses, lack of context, and outdated information. Nonetheless, the ability for these LLM chatbots to address these inquires has largely yet to be evaluated and will be critical for understanding the risks and opportunities of LLM chatbots in orthopedics.
    MeSH term(s) Humans ; Orthopedics
    Language English
    Publishing date 2024-01-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 605542-4
    ISSN 1554-527X ; 0736-0266
    ISSN (online) 1554-527X
    ISSN 0736-0266
    DOI 10.1002/jor.25782
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  5. Article ; Online: Elemental Testing Using Inductively Coupled Plasma Mass Spectrometry in Clinical Laboratories.

    Yang, He S / LaFrance, Delecia R / Hao, Ying

    American journal of clinical pathology

    2021  Volume 156, Issue 2, Page(s) 167–175

    Abstract: Objectives: This review aims to describe the principles underlying different types of inductively coupled plasma mass spectrometry (ICP-MS), and major technical advancements that reduce spectral interferences, as well as their suitability and wide ... ...

    Abstract Objectives: This review aims to describe the principles underlying different types of inductively coupled plasma mass spectrometry (ICP-MS), and major technical advancements that reduce spectral interferences, as well as their suitability and wide applications in clinical laboratories.
    Methods: A literature survey was performed to review the technical aspects of ICP-MS, ICP-MS/MS, high-resolution ICP-MS, and their applications in disease diagnosis and monitoring.
    Results: Compared to the atomic absorption spectrometry and ICP-optical emission spectrometry, ICP-MS has advantages including improved precision, sensitivity and accuracy, wide linear dynamic range, multielement measurement capability, and ability to perform isotopic analysis. Technical advancements, such as collision/reaction cells, triple quadrupole ICP-MS, and sector-field ICP-MS, have been introduced to improve resolving power and reduce interferences. Cases are discussed that highlight the clinical applications of ICP-MS including determination of toxic elements, quantification of nutritional elements, monitoring elemental deficiency in metabolic disease, and multielement analysis.
    Conclusions: This review provides insight on the strategies of elemental analysis in clinical laboratories and demonstrates current and emerging clinical applications of ICP-MS.
    MeSH term(s) Humans ; Laboratories ; Spectrophotometry, Atomic/instrumentation ; Spectrophotometry, Atomic/methods
    Language English
    Publishing date 2021-06-17
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2944-0
    ISSN 1943-7722 ; 0002-9173
    ISSN (online) 1943-7722
    ISSN 0002-9173
    DOI 10.1093/ajcp/aqab013
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  6. Article: Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond.

    Xie, Qianqian / Schenck, Edward J / Yang, He S / Chen, Yong / Peng, Yifan / Wang, Fei

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Artificial intelligence (AI), especially the most recent large language models (LLMs), holds great promise in healthcare and medicine, with applications spanning from biological scientific discovery and clinical patient care to public health policymaking. ...

    Abstract Artificial intelligence (AI), especially the most recent large language models (LLMs), holds great promise in healthcare and medicine, with applications spanning from biological scientific discovery and clinical patient care to public health policymaking. However, AI methods have the critical concern for generating factually incorrect or unfaithful information, posing potential long-term risks, ethical issues, and other serious consequences. This review aims to provide a comprehensive overview of the faithfulness problem in existing research on AI in healthcare and medicine, with a focus on the analysis of the causes of unfaithful results, evaluation metrics, and mitigation methods. We systematically reviewed the recent progress in optimizing the factuality across various generative medical AI methods, including knowledge-grounded LLMs, text-to-text generation, multimodality-to-text generation, and automatic medical fact-checking tasks. We further discussed the challenges and opportunities of ensuring the faithfulness of AI-generated information in these applications. We expect that this review will assist researchers and practitioners in understanding the faithfulness problem in AI-generated information in healthcare and medicine, as well as the recent progress and challenges in related research. Our review can also serve as a guide for researchers and practitioners who are interested in applying AI in medicine and healthcare.
    Language English
    Publishing date 2023-07-01
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.04.18.23288752
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond.

    Xie, Qianqian / Schenck, Edward J / Yang, He S / Chen, Yong / Peng, Yifan / Wang, Fei

    Research square

    2023  

    Abstract: Objective: While artificial intelligence (AI), particularly large language models (LLMs), offers significant potential for medicine, it raises critical concerns due to the possibility of generating factually incorrect information, leading to potential ... ...

    Abstract Objective: While artificial intelligence (AI), particularly large language models (LLMs), offers significant potential for medicine, it raises critical concerns due to the possibility of generating factually incorrect information, leading to potential long-term risks and ethical issues. This review aims to provide a comprehensive overview of the faithfulness problem in existing research on AI in healthcare and medicine, with a focus on the analysis of the causes of unfaithful results, evaluation metrics, and mitigation methods.
    Materials and methods: Using PRISMA methodology, we sourced 5,061 records from five databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, Google Scholar) published between January 2018 to March 2023. We removed duplicates and screened records based on exclusion criteria.
    Results: With 40 leaving articles, we conducted a systematic review of recent developments aimed at optimizing and evaluating factuality across a variety of generative medical AI approaches. These include knowledge-grounded LLMs, text-to-text generation, multimodality-to-text generation, and automatic medical fact-checking tasks.
    Discussion: Current research investigating the factuality problem in medical AI is in its early stages. There are significant challenges related to data resources, backbone models, mitigation methods, and evaluation metrics. Promising opportunities exist for novel faithful medical AI research involving the adaptation of LLMs and prompt engineering.
    Conclusion: This comprehensive review highlights the need for further research to address the issues of reliability and factuality in medical AI, serving as both a reference and inspiration for future research into the safe, ethical use of AI in medicine and healthcare.
    Language English
    Publishing date 2023-12-04
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3661764/v1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: AI Chatbots in Clinical Laboratory Medicine: Foundations and Trends.

    Yang, He S / Wang, Fei / Greenblatt, Matthew B / Huang, Sharon X / Zhang, Yi

    Clinical chemistry

    2023  Volume 69, Issue 11, Page(s) 1238–1246

    Abstract: Background: Artificial intelligence (AI) conversational agents, or chatbots, are computer programs designed to simulate human conversations using natural language processing. They offer diverse functions and applications across an expanding range of ... ...

    Abstract Background: Artificial intelligence (AI) conversational agents, or chatbots, are computer programs designed to simulate human conversations using natural language processing. They offer diverse functions and applications across an expanding range of healthcare domains. However, their roles in laboratory medicine remain unclear, as their accuracy, repeatability, and ability to interpret complex laboratory data have yet to be rigorously evaluated.
    Content: This review provides an overview of the history of chatbots, two major chatbot development approaches, and their respective advantages and limitations. We discuss the capabilities and potential applications of chatbots in healthcare, focusing on the laboratory medicine field. Recent evaluations of chatbot performance are presented, with a special emphasis on large language models such as the Chat Generative Pre-trained Transformer in response to laboratory medicine questions across different categories, such as medical knowledge, laboratory operations, regulations, and interpretation of laboratory results as related to clinical context. We analyze the causes of chatbots' limitations and suggest research directions for developing more accurate, reliable, and manageable chatbots for applications in laboratory medicine.
    Summary: Chatbots, which are rapidly evolving AI applications, hold tremendous potential to improve medical education, provide timely responses to clinical inquiries concerning laboratory tests, assist in interpreting laboratory results, and facilitate communication among patients, physicians, and laboratorians. Nevertheless, users should be vigilant of existing chatbots' limitations, such as misinformation, inconsistencies, and lack of human-like reasoning abilities. To be effectively used in laboratory medicine, chatbots must undergo extensive training on rigorously validated medical knowledge and be thoroughly evaluated against standard clinical practice.
    MeSH term(s) Humans ; Laboratories, Clinical ; Artificial Intelligence ; Laboratories ; Clinical Laboratory Services ; Medicine
    Language English
    Publishing date 2023-09-04
    Publishing country England
    Document type Review ; Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1093/clinchem/hvad106
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  9. Article ; Online: Assessing the Accuracy and Clinical Utility of ChatGPT in Laboratory Medicine.

    Munoz-Zuluaga, Carlos / Zhao, Zhen / Wang, Fei / Greenblatt, Matthew B / Yang, He S

    Clinical chemistry

    2023  Volume 69, Issue 8, Page(s) 939–940

    Language English
    Publishing date 2023-05-10
    Publishing country England
    Document type Letter
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1093/clinchem/hvad058
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  10. Article ; Online: A rare case of persistent pseudohypobicarbonatemia arising from chemistry analyzer-specific interference.

    Ma, Lucy / Zhao, Zhen / Racine-Brzostek, Sabrina E / Yang, He S

    Clinica chimica acta; international journal of clinical chemistry

    2021  Volume 519, Page(s) 308–310

    Abstract: Background: Major discrepancies between concentrations of serum total carbon dioxide (tCO: Case: A 75-year-old man with persistent fevers was found to have a low concentration of serum tCO: Results: Mixing studies revealed non-linearity of serum ... ...

    Abstract Background: Major discrepancies between concentrations of serum total carbon dioxide (tCO
    Case: A 75-year-old man with persistent fevers was found to have a low concentration of serum tCO
    Results: Mixing studies revealed non-linearity of serum tCO
    Conclusion: Laboratory professionals should be aware that spuriously low serum tCO
    MeSH term(s) Acidosis ; Aged ; Bicarbonates ; Blood Gas Analysis ; Carbon Dioxide ; Humans ; Male ; Paraproteins
    Chemical Substances Bicarbonates ; Paraproteins ; Carbon Dioxide (142M471B3J)
    Language English
    Publishing date 2021-05-26
    Publishing country Netherlands
    Document type Case Reports ; Journal Article
    ZDB-ID 80228-1
    ISSN 1873-3492 ; 0009-8981
    ISSN (online) 1873-3492
    ISSN 0009-8981
    DOI 10.1016/j.cca.2021.05.025
    Database MEDical Literature Analysis and Retrieval System OnLINE

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