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Hou H, Zhang R, Li J. Artificial intelligence in the clinical laboratory. Clin Chim Acta 2024; 559:119724. [PMID: 38734225 DOI: 10.1016/j.cca.2024.119724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024]
Abstract
Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.
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Affiliation(s)
- Hanjing Hou
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
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Çubukçu HC, Topcu Dİ, Yenice S. Machine learning-based clinical decision support using laboratory data. Clin Chem Lab Med 2024; 62:793-823. [PMID: 38015744 DOI: 10.1515/cclm-2023-1037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.
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Affiliation(s)
- Hikmet Can Çubukçu
- General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye
- Hacettepe University Institute of Informatics, Ankara, Türkiye
| | - Deniz İlhan Topcu
- Health Sciences University İzmir Tepecik Education and Research Hospital, Medical Biochemistry, İzmir, Türkiye
| | - Sedef Yenice
- Florence Nightingale Hospital, Istanbul, Türkiye
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Ialongo C, Pieri M. Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical phase: comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436-41. Clin Chem Lab Med 2024; 0:cclm-2024-0210. [PMID: 38501458 DOI: 10.1515/cclm-2024-0210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/29/2024] [Indexed: 03/20/2024]
Affiliation(s)
- Cristiano Ialongo
- Department of Experimental Medicine, 9311 University of Rome La Sapienza , Rome, Italy
| | - Massimo Pieri
- Department of Experimental Medicine and Surgery, 9318 University of Rome "Tor Vergata" , Rome, Italy
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Turčić A, Štajduhar A, Vogrinc Ž, Zaninović L, Rogić D. Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. Clin Chem Lab Med 2024; 62:436-441. [PMID: 37782817 DOI: 10.1515/cclm-2023-1013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023]
Abstract
OBJECTIVES To create a supervised machine learning algorithm aimed at predicting an optimal cerebrospinal fluid (CSF) dilution when determining virus specific antibody indices to reduce the need for repeated tests. METHODS The CatBoost model was trained, optimized, and tested on a dataset with five input variables: albumin quotient, immunoglobulin G (IgG) in CSF, IgG quotient (QIgG), intrathecal synthesis (ITS) and limes quotient (LIM IgG). Albumin and IgG concentrations in CSF and serum were performed by immunonephelometry on Atellica NEPH 630 (Siemens Healthineers, Erlangen, Germany) and ITS and LIM IgG were calculated according to Reiber. Concentrations of IgG antibodies to measles, rubella, varicella zoster and herpes simplex 1/2 viruses were analysed in CSF and serum by ELISA (Euroimmun, Lübeck, Germany). Optimal CSF dilution was defined for each virus and used as a classification variable while the standard operating procedure was set to start at 2×-dilution of CSF. RESULTS The dataset included 571 samples with the imbalanced distribution of the optimal CSF dilutions: 2× dilution n=440, 3× dilution n=109, 4× dilution n=22. The optimized CatBoost model achieved an area under the curve (AUC) score of 0.971, and a test accuracy of 0.900. The model falsely classified 14 (9.9 %) samples of the testing set but reduced the need for repeated testing compared to the standard protocol by 42 %. The output of the CatBoost model is mostly dependant on the QIgG, ITS and CSF IgG variables. CONCLUSIONS An accurate algorithm was achieved for predicting the optimal CSF dilution, which reduces the number of test repeats.
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Affiliation(s)
- Ana Turčić
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Andrija Štajduhar
- Andrija Štampar School of Public Health, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Željka Vogrinc
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Ljiljana Zaninović
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Dunja Rogić
- Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
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Jafri L, Farooqui AJ, Grant J, Omer U, Gale R, Ahmed S, Khan AH, Siddiqui I, Ghani F, Majid H. Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices. BMC MEDICAL EDUCATION 2024; 24:170. [PMID: 38389053 PMCID: PMC10882878 DOI: 10.1186/s12909-024-05078-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/21/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is gradually transforming the practises of healthcare providers. Over the last two decades, the advent of AI into numerous aspects of pathology has opened transformative possibilities in how we practise laboratory medicine. Objectives of this study were to explore how AI could impact the clinical practices of professionals working in Clinical Chemistry laboratories, while also identifying effective strategies in medical education to facilitate the required changes. METHODS From March to August 2022, an exploratory qualitative study was conducted at the Section of Clinical Chemistry, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan, in collaboration with Keele University, Newcastle, United Kingdom. Semi-structured interviews were conducted to collect information from diverse group of professionals working in Clinical Chemistry laboratories. All interviews were audio recorded and transcribed verbatim. They were asked what changes AI would involve in the laboratory, what resources would be necessary, and how medical education would assist them in adapting to the change. A content analysis was conducted, resulting in the development of codes and themes based on the analyzed data. RESULTS The interviews were analysed to identify three primary themes: perspectives and considerations for AI adoption, educational and curriculum adjustments, and implementation techniques. Although the use of diagnostic algorithms is currently limited in Pakistani Clinical Chemistry laboratories, the application of AI is expanding. All thirteen participants stated their reasons for being hesitant to use AI. Participants stressed the importance of critical aspects for effective AI deployment, the need of a collaborative integrative approach, and the need for constant horizon scanning to keep up with AI developments. CONCLUSIONS Three primary themes related to AI adoption were identified: perspectives and considerations, educational and curriculum adjustments, and implementation techniques. The study's findings give a sound foundation for making suggestions to clinical laboratories, scientific bodies, and national and international Clinical Chemistry and laboratory medicine organisations on how to manage pathologists' shifting practises because of AI.
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Affiliation(s)
- Lena Jafri
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan.
| | - Arsala Jameel Farooqui
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Janet Grant
- Centre for Medical Education in Context [CenMEDIC], CenMEDIC, 27 Church Street, TW12 2EB, Hampton, Middlesex, UK
| | | | - Rodney Gale
- Centre for Medical Education in Context [CenMEDIC], CenMEDIC, 27 Church Street, TW12 2EB, Hampton, Middlesex, UK
| | - Sibtain Ahmed
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Aysha Habib Khan
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Imran Siddiqui
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Farooq Ghani
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
| | - Hafsa Majid
- Section of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, 74800, Karachi, Pakistan
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Lippi G, Mattiuzzi C, Favaloro EJ. Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives. J Med Biochem 2024; 43:1-10. [PMID: 38496022 PMCID: PMC10943465 DOI: 10.5937/jomb0-45936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 08/24/2023] [Indexed: 03/19/2024] Open
Abstract
The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.
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Affiliation(s)
- Giuseppe Lippi
- University of Verona, Section of Clinical Biochemistry and School of Medicine, Verona, Italy
| | - Camilla Mattiuzzi
- Hospital of Rovereto, Provincial Agency for Social and Sanitary Services (APSS), Medical Direction, Trento, Italy
| | - Emmanuel J. Favaloro
- Institute of Clinical Pathology and Medical Research (ICPMR), Sydney Centres for Thrombosis and Haemostasis, Department of Haematology, NSW Health Pathology, Westmead Hospital, Westmead, NSW Australia
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Devis L, Catry E, Honore PM, Mansour A, Lippi G, Mullier F, Closset M. Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review. Ann Intensive Care 2024; 14:9. [PMID: 38224401 PMCID: PMC10789714 DOI: 10.1186/s13613-024-01244-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Healthcare expenses are increasing, as is the utilization of laboratory resources. Despite this, between 20% and 40% of requested tests are deemed inappropriate. Improper use of laboratory resources leads to unwanted consequences such as hospital-acquired anemia, infections, increased costs, staff workload and patient stress and discomfort. The most unfavorable consequences result from unnecessary follow-up tests and treatments (overuse) and missed or delayed diagnoses (underuse). In this context, several interventions have been carried out to improve the appropriateness of laboratory testing. To date, there have been few published assessments of interventions specific to the intensive care unit. We reviewed the literature for interventions implemented in the ICU to improve the appropriateness of laboratory testing. We searched literature from 2008 to 2023 in PubMed, Embase, Scopus, and Google Scholar databases between April and June 2023. Five intervention categories were identified: education and guidance (E&G), audit and feedback, gatekeeping, computerized physician order entry (including reshaping of ordering panels), and multifaceted interventions (MFI). We included a sixth category exploring the potential role of artificial intelligence and machine learning (AI/ML)-based assisting tools in such interventions. E&G-based interventions and MFI are the most frequently used approaches. MFI is the most effective type of intervention, and shows the strongest persistence of effect over time. AI/ML-based tools may offer valuable assistance to the improvement of appropriate laboratory testing in the near future. Patient safety outcomes are not impaired by interventions to reduce inappropriate testing. The literature focuses mainly on reducing overuse of laboratory tests, with only one intervention mentioning underuse. We highlight an overall poor quality of methodological design and reporting and argue for standardization of intervention methods. Collaboration between clinicians and laboratory staff is key to improve appropriate laboratory utilization. This article offers practical guidance for optimizing the effectiveness of an intervention protocol designed to limit inappropriate use of laboratory resources.
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Affiliation(s)
- Luigi Devis
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Emilie Catry
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Patrick M Honore
- Department of Intensive Care, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Alexandre Mansour
- Department of Anesthesia and Critical Care, Pontchaillou University Hospital of Rennes, Rennes, France
- IRSET-INSERM-1085, Univ Rennes, Rennes, France
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - François Mullier
- Department of Laboratory Medicine, Hematology, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Namur Thrombosis and Hemostasis Center (NTHC), Namur Research Institute for Life Sciences (NARILIS), Namur, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Mélanie Closset
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium.
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium.
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Hou HX, Li A, Thierauf JC, Lennerz JK. Diagnostic Test Utilization Management Strategies as an Opportunity for Equitable Access to Molecularly Informed Clinical Care. J Appl Lab Med 2024; 9:41-49. [PMID: 38167770 DOI: 10.1093/jalm/jfad079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/25/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Companion diagnostics are an essential component of oncology. Timing, cost, and adaptability to new drug/biomarker approvals represent challenges in assuring value-based care. Overcoming these challenges requires strategies for equitable access and efficient integration. METHODS Based on prior laboratory improvements and payor policy implementations, we define equitable access in laboratory testing and conceptualized a framework for initiatives that optimize diagnostic performance. RESULTS We define equitable access as an imperative goal seeking to remove disparities that may arise due to financial hardships, geographical isolation, cultural differences, or other social determinants of health. We distinguish (a) utilization, as the practice pattern of ordered tests, (b) utilization management, as the evidence-based guidance of the utilization decisions, and (c) utilization management strategies, defined as the tools and techniques used to influence decision-making. These 3 dimensions establish a standardized vocabulary to clarify equitable alignment of strategies in specific care pathways. Alignment of logistic, administrative, and financial incentive structures is paramount when creating sustainable personalized care pathway programs. CONCLUSIONS Strategies to accomplish equitable and meaningful use of diagnostic tests can help enhance access to timely and accurate diagnoses, ultimately leading to improved patient outcomes.
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Affiliation(s)
- Helen X Hou
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Annie Li
- Department of Pathology, Center for Integrated Diagnostics Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States
| | - Julia C Thierauf
- Department of Pathology, Center for Integrated Diagnostics Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States
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Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan. BMC Med Inform Decis Mak 2023; 23:288. [PMID: 38098095 PMCID: PMC10722664 DOI: 10.1186/s12911-023-02403-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
INTRODUCTION The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions. METHODS An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores. RESULTS Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps. CONCLUSIONS This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.
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Affiliation(s)
- Walid Al-Qerem
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan.
| | - Judith Eberhardt
- School of Social Sciences, Humanities and Law, Department of Psychology, Teesside University, TS1 3BX, Middlesbrough, UK
| | - Anan Jarab
- College of Pharmacy, Al Ain University, 64141, Abu Dhabi, UAE
- AAU Health and Biomedical Research Center, Al Ain University, 112612, Abu Dhabi, United Arab Emirates
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, 22110, Irbid, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Alaa Hammad
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Fawaz Alasmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 12372, Riyadh, Saudi Arabia
| | - Badi'ah Alazab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Daoud Abu Husein
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Jumana Alazab
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
| | - Saed Al-Beool
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
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Barallat J, Gómez C, Sancho-Cerro A. AI, diabetes and getting lost in translation: a multilingual evaluation of Bing with ChatGPT focused in HbA 1c. Clin Chem Lab Med 2023; 61:e222-e224. [PMID: 37155932 DOI: 10.1515/cclm-2023-0295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/28/2023] [Indexed: 05/10/2023]
Affiliation(s)
- Jaume Barallat
- Biochemistry Department, LCMN, Germans Trias i Pujol University Hospital Badalona, Spain
| | - Carolina Gómez
- Biochemistry Department, LCMN, Germans Trias i Pujol University Hospital Badalona, Spain
| | - Ana Sancho-Cerro
- Biochemistry Department, LCMN, Germans Trias i Pujol University Hospital Badalona, Spain
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Cadamuro J, Cabitza F, Debeljak Z, De Bruyne S, Frans G, Perez SM, Ozdemir H, Tolios A, Carobene A, Padoan A. Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI). Clin Chem Lab Med 2023; 61:1158-1166. [PMID: 37083166 DOI: 10.1515/cclm-2023-0355] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVES ChatGPT, a tool based on natural language processing (NLP), is on everyone's mind, and several potential applications in healthcare have been already proposed. However, since the ability of this tool to interpret laboratory test results has not yet been tested, the EFLM Working group on Artificial Intelligence (WG-AI) has set itself the task of closing this gap with a systematic approach. METHODS WG-AI members generated 10 simulated laboratory reports of common parameters, which were then passed to ChatGPT for interpretation, according to reference intervals (RI) and units, using an optimized prompt. The results were subsequently evaluated independently by all WG-AI members with respect to relevance, correctness, helpfulness and safety. RESULTS ChatGPT recognized all laboratory tests, it could detect if they deviated from the RI and gave a test-by-test as well as an overall interpretation. The interpretations were rather superficial, not always correct, and, only in some cases, judged coherently. The magnitude of the deviation from the RI seldom plays a role in the interpretation of laboratory tests, and artificial intelligence (AI) did not make any meaningful suggestion regarding follow-up diagnostics or further procedures in general. CONCLUSIONS ChatGPT in its current form, being not specifically trained on medical data or laboratory data in particular, may only be considered a tool capable of interpreting a laboratory report on a test-by-test basis at best, but not on the interpretation of an overall diagnostic picture. Future generations of similar AIs with medical ground truth training data might surely revolutionize current processes in healthcare, despite this implementation is not ready yet.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Federico Cabitza
- DISCo, Università degli Studi di Milano-Bicocca, Milano, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Zeljko Debeljak
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
- Clinical Institute of Laboratory Diagnostics, University Hospital Center Osijek, Osijek, Croatia
| | - Sander De Bruyne
- Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Glynis Frans
- Department of Laboratory Medicine, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Salomon Martin Perez
- Unidad de Bioquímica Clínica, Hospital Universitario Virgen Macarena, Sevilla, Spain
| | - Habib Ozdemir
- Department of Medical Biochemistry, Faculty of Medicine, Manisa Celal Bayar University, Manisa, Türkiye
| | - Alexander Tolios
- Department of Transfusion Medicine and Cell Therapy, Medical University of Vienna, Vienna, Austria
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Padoan
- Department of Medicine (DIMED), University of Padova, Padova, Italy
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Galozzi P, Basso D, Plebani M, Padoan A. Artificial Intelligence and laboratory data in rheumatic diseases. Clin Chim Acta 2023; 546:117388. [PMID: 37187221 DOI: 10.1016/j.cca.2023.117388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.
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Affiliation(s)
- Paola Galozzi
- Department of Medicine-DIMED, University of Padova, Padova, Italy.
| | - Daniela Basso
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
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Lennerz JK, Salgado R, Kim GE, Sirintrapun SJ, Thierauf JC, Singh A, Indave I, Bard A, Weissinger SE, Heher YK, de Baca ME, Cree IA, Bennett S, Carobene A, Ozben T, Ritterhouse LL. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin Chem Lab Med 2023; 61:544-557. [PMID: 36696602 DOI: 10.1515/cclm-2022-1151] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
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Affiliation(s)
- Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Grace E Kim
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | | | - Julia C Thierauf
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
- Department of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, Heidelberg, Germany
| | - Ankit Singh
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Iciar Indave
- European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), Lisbon, Portugal
| | - Adam Bard
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Yael K Heher
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Ian A Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - Shannon Bennett
- Department of Laboratory Medicine and Pathology (DLMP), Mayo Clinic, Rochester, MN, USA
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tomris Ozben
- Medical Faculty, Dept. of Clinical Biochemistry, Akdeniz University, Antalya, Türkiye
- Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
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Ozben T. SMART and GREEN LABORATORIES. How to implement IVDR, emerging technologies and sustainable practices in medical laboratories? Clin Chem Lab Med 2023; 61:531-534. [PMID: 36749317 DOI: 10.1515/cclm-2023-0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Tomris Ozben
- Medical Faculty, Department of Medical Biochemistry, Akdeniz University, Antalya, Türkiye.,Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
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