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Plebani M. Harmonizing the post-analytical phase: focus on the laboratory report. Clin Chem Lab Med 2024; 62:1053-1062. [PMID: 38176022 DOI: 10.1515/cclm-2023-1402] [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: 12/06/2023] [Accepted: 12/27/2023] [Indexed: 01/06/2024]
Abstract
The final, post-analytical, phase of laboratory testing is increasingly recognized as a fundamental step in maximizing quality and effectiveness of laboratory information. There is a need to close the loop of the total testing cycle by improving upon the laboratory report, and its notification to users. The harmonization of the post-analytical phase is somewhat complicated, mainly because it calls for communication that involves parties speaking different languages, including laboratorians, physicians, information technology specialists, and patients. Recently, increasing interest has been expressed in integrated diagnostics, defined as convergence of imaging, pathology, and laboratory tests with advanced information technology (IT). In particular, a common laboratory, radiology and pathology diagnostic reporting system that integrates text, sentinel images and molecular diagnostic data to an integrated, coherent interpretation enhances management decisions and improves quality of care.
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Affiliation(s)
- Mario Plebani
- Clinical Biochemistry and Clinical Molecular Biology, University of Padova, Padova, Italy
- Department of Pathology, University of Texas, Medical Branch, Galveston, TX, USA
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2
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Blatter TU, Witte H, Fasquelle-Lopez J, Theodoros Naka C, Raisaro JL, Leichtle AB. The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application. J Med Internet Res 2023; 25:e47254. [PMID: 37851984 PMCID: PMC10620636 DOI: 10.2196/47254] [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: 03/13/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. OBJECTIVE This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. METHODS A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. RESULTS The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. CONCLUSIONS With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine.
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Affiliation(s)
- Tobias Ueli Blatter
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Harald Witte
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
| | | | - Christos Theodoros Naka
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
- Laboratory of Biometry, University of Thessaly, Volos, Greece
| | - Jean Louis Raisaro
- Biomedical Data Science Center, University Hospital Lausanne, Lausanne, Switzerland
| | - Alexander Benedikt Leichtle
- University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland
- Center for Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland
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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: 26] [Impact Index Per Article: 26.0] [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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Klawitter S, Hoffmann G, Holdenrieder S, Kacprowski T, Klawonn F. A zlog-based algorithm and tool for plausibility checks of reference intervals. Clin Chem Lab Med 2023; 61:260-265. [PMID: 36321255 DOI: 10.1515/cclm-2022-0688] [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: 07/16/2022] [Accepted: 10/18/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Laboratory information systems typically contain hundreds or even thousands of reference limits stratified by sex and age. Since under these conditions a manual plausibility check is hardly feasible, we have developed a simple algorithm that facilitates this check. An open-source R tool is available as a Shiny application at github.com/SandraKla/Zlog_AdRI. METHODS Based on the zlog standardization, we can possibly detect critical jumps at the transitions between age groups, regardless of the analytical method or the measuring unit. Its advantage compared to the standard z-value is that means and standard deviations are calculated from the reference limits rather than from the underlying data itself. The purpose of the tool is illustrated by the example of reference intervals of children and adolescents from the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER). RESULTS The Shiny application identifies the zlog values, lists them in a colored table format and plots them additionally with the specified reference intervals. The algorithm detected several strong and rapid changes in reference intervals from the neonatal period to puberty. Remarkable jumps with absolute zlog values of more than five were seen for 29 out of 192 reference limits (15.1%). This might be attenuated by introducing shorter time periods or mathematical functions of reference limits over age. CONCLUSIONS Age-partitioned reference intervals will remain the standard in laboratory routine for the foreseeable future, and as such, algorithmic approaches like our zlog approach in the presented Shiny application will remain valuable tools for testing their plausibility on a wide scale.
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Affiliation(s)
- Sandra Klawitter
- Trillium GmbH Medizinischer Fachverlag, Grafrath, Germany
- Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
| | - Georg Hoffmann
- Trillium GmbH Medizinischer Fachverlag, Grafrath, Germany
- German Heart Center at the Technical University Munich, Institute of Laboratory Medicine, München, Germany
| | - Stefan Holdenrieder
- German Heart Center at the Technical University Munich, Institute of Laboratory Medicine, München, Germany
| | - Tim Kacprowski
- Peter L. Reichertz Institute for Medical Informatics of Technical University of Braunschweig and Hanover Medical School, Division Data Science in Biomedicine, Braunschweig, Germany
- Technical University of Braunschweig, Braunschweig Integrated Centre of Systems Biology, Braunschweig, Germany
| | - Frank Klawonn
- Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Helmholtz Centre for Infection Research, Biostatistics, Braunschweig, Germany
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Cadamuro J, Simundic AM, von Meyer A, Haschke-Becher E, Keppel MH, Oberkofler H, Felder TK, Mrazek C. Diagnostic Workup of Microcytic Anemia: An Evaluation of Underuse or Misuse of Laboratory Testing in a Hospital Setting Using the AlinIQ System. Arch Pathol Lab Med 2023; 147:117-124. [PMID: 35472855 DOI: 10.5858/arpa.2021-0283-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/19/2021] [Indexed: 12/31/2022]
Abstract
CONTEXT.— Underuse of laboratory testing has been previously investigated in preselected populations, such as documented malpractice claims. However, these numbers might not reflect real-life situations. OBJECTIVE.— To evaluate the underuse and misuse of laboratory follow-up testing in a real-life hospital patient population with microcytic anemia, using laboratory results ordered during routine patient care. DESIGN.— From all patients in whom a microcytic anemia was detected during routine diagnostics in 2018, all available laboratory data were collected and screened for appropriateness of diagnostic workup of iron deficiency and thalassemia. Subgroup analysis was performed for patient groups with mean corpuscular volume values 75 to 79 μm3 (group 1), 65 to 74 μm3 (group 2), and <65 μm3 (group 3). RESULTS.— A total of 2244 patients with microcytic anemia were identified. Follow-up testing for iron deficiency was not performed in 761 cases (34%). For inconclusive ferritin levels due to elevated C-reactive protein results (n = 336), reticulocyte hemoglobin content or soluble transferrin receptor levels were missing in 86 cases (26%). In patients with suspected thalassemia (n = 127), follow-up testing for hemoglobin variants was not performed in 70 cases (55%). Subgroup analysis showed that the frequency of underuse of iron status as well as thalassemia/hemoglobinopathy testing decreased from group 1 to group 3. When considering relevant preexisting anemia diagnoses, laboratory tests were underused in 904 cases (40.3%). CONCLUSIONS.— Because 40% (n = 904) of the patients with microcytic anemia were potentially not followed up correctly, laboratory specialists are advised to act by implementing demand management strategies in collaboration with clinicians to overcome underuse of laboratory tests and to improve patient safety.
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Affiliation(s)
- Janne Cadamuro
- From the Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria (Cadamuro, Haschke-Becher, Keppel, Oberkofler, Felder, Mrazek)
| | - Ana-Maria Simundic
- The Department of Medical Laboratory Diagnostics, Faculty of Pharmacy and Biochemistry, University Hospital Sveti Duh, University of Zagreb, Zagreb, Croatia (Simundic)
| | - Alexander von Meyer
- The Institute for Laboratory Medicine and Medical Microbiology, München Clinic, Munich, Germany (von Meyer)
| | - Elisabeth Haschke-Becher
- From the Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria (Cadamuro, Haschke-Becher, Keppel, Oberkofler, Felder, Mrazek)
| | - Martin H Keppel
- From the Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria (Cadamuro, Haschke-Becher, Keppel, Oberkofler, Felder, Mrazek)
| | - Hannes Oberkofler
- From the Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria (Cadamuro, Haschke-Becher, Keppel, Oberkofler, Felder, Mrazek)
| | - Thomas K Felder
- From the Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria (Cadamuro, Haschke-Becher, Keppel, Oberkofler, Felder, Mrazek)
| | - Cornelia Mrazek
- From the Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria (Cadamuro, Haschke-Becher, Keppel, Oberkofler, Felder, Mrazek)
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Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics (Basel) 2022; 12:diagnostics12081923. [PMID: 36010273 PMCID: PMC9406962 DOI: 10.3390/diagnostics12081923] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 12/22/2022] Open
Abstract
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
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Cadamuro J, Winzer J, Perkhofer L, von Meyer A, Bauça JM, Plekhanova O, Linko-Parvinen A, Watine J, Kniewallner KM, Keppel MH, Šálek T, Mrazek C, Felder TK, Oberkofler H, Haschke-Becher E, Vermeersch P, Kristoffersen AH, Eisl C. Efficiency, efficacy and subjective user satisfaction of alternative laboratory report formats. An investigation on behalf of the Working Group for Postanalytical Phase (WG-POST), of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM). Clin Chem Lab Med 2022; 60:1356-1364. [PMID: 35696446 DOI: 10.1515/cclm-2022-0269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/31/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Although laboratory result presentation may lead to information overload and subsequent missed or delayed diagnosis, little has been done in the past to improve this post-analytical issue. We aimed to investigate the efficiency, efficacy and user satisfaction of alternative report formats. METHODS We redesigned cumulative (sparkline format) and single reports (improved tabular and z-log format) and tested these on 46 physicians, nurses and medical students in comparison to the classical tabular formats, by asking standardized questions on general items on the reports as well as on suspected diagnosis and follow-up treatment or diagnostics. RESULTS Efficacy remained at a very high level both in the new formats as well as in the classical formats. We found no significant difference in any of the groups. Efficiency improved in all groups when using the sparkline cumulative format and marginally when showing the improved tabular format. When asking medical questions, efficiency and efficacy remained similar between report formats and groups. All alternative reports were subjectively more attractive to the majority of participants. CONCLUSIONS Showing cumulative reports as a graphical display led to faster detection of general information on the report with the same level of correctness. Considering the familiarity bias of the classical single report formats, the borderline-significant improvement of the alternative tabular format and the non-inferiority of the z-log format, suggests that single reports might benefit from some improvements derived from basic information design.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Johannes Winzer
- School of Business & Management, University of Applied Sciences Upper Austria, Steyr, Austria
| | - Lisa Perkhofer
- School of Business & Management, University of Applied Sciences Upper Austria, Steyr, Austria
| | - Alexander von Meyer
- Institute for Laboratory Medicine and Medical Microbiology, Medizet, Munich, Germany
| | - Josep M Bauça
- Department of Laboratory Medicine, Hospital Universitari Son Espases, Palma, Spain
| | - Olga Plekhanova
- Laboratory Diagnostics Center, Moscow Healthcare Department, Moscow, Russia
| | - Anna Linko-Parvinen
- Clinical Chemistry, Tyks Laboratories, Turku University Hospital, Turku, Finland.,Department of Clinical Chemistry, University of Turku, Turku, Finland
| | - Joseph Watine
- Laboratoire de Biologie Médicale, Hôpital de Villefranche-de-Rouergue, Villefranche-de-Rouergue, France
| | - Kathrin Maria Kniewallner
- Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TreCS), Paracelsus Medical University, Salzburg, Austria
| | - Martin Helmut Keppel
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Tomáš Šálek
- Department of Clinical biochemistry and pharmacology, The Tomas Bata Hospital in Zlín, Zlín, The Czech Republic
| | - Cornelia Mrazek
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Thomas Klaus Felder
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Hannes Oberkofler
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | | | | | - Ann Helen Kristoffersen
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital and Noklus, Haraldsplass Deaconess Hospital, Bergen, Norway
| | - Christoph Eisl
- School of Business & Management, University of Applied Sciences Upper Austria, Steyr, Austria
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Cadamuro J. Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review. Diagnostics (Basel) 2021; 11:1399. [PMID: 34441333 PMCID: PMC8392825 DOI: 10.3390/diagnostics11081399] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/05/2021] [Accepted: 07/21/2021] [Indexed: 01/04/2023] Open
Abstract
Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, A-5020 Salzburg, Austria
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