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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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Carobene A, Cabitza F, Bernardini S, Gopalan R, Lennerz JK, Weir C, Cadamuro J. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories. Clin Chem Lab Med 2023; 61:535-543. [PMID: 36327445 DOI: 10.1515/cclm-2022-1030] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.
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Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Cabitza
- IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
| | - Sergio Bernardini
- Unit of Laboratory Medicine, Tor Vergata University Hospital, Rome, Italy.,Department of Experimental Medicine, University of Tor Vergata, Rome, Italy
| | - Raj Gopalan
- Siemens Healthcare Diagnostics, Siemens Healthineers, Malvern, PA, USA
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | | | - Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
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Rabbani N, Kim GYE, Suarez CJ, Chen JH. Applications of machine learning in routine laboratory medicine: Current state and future directions. Clin Biochem 2022; 103:1-7. [PMID: 35227670 PMCID: PMC9007900 DOI: 10.1016/j.clinbiochem.2022.02.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/04/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023]
Abstract
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.
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Affiliation(s)
- Naveed Rabbani
- Department of Clinical Informatics, Lucile Packard Children's Hospital, Palo Alto, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Grace Y E Kim
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jonathan H Chen
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA; Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA
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