Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories
39
Zitationen
7
Autoren
2022
Jahr
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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.663 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.576 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.091 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.859 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
Institutionen
- Vita-Salute San Raffaele University(IT)
- Istituti di Ricovero e Cura a Carattere Scientifico(IT)
- Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele
- Istituto Clinico Sant'Ambrogio(IT)
- University of Milano-Bicocca(IT)
- University of Rome Tor Vergata(IT)
- Siemens Healthcare (United States)(US)
- Harvard University(US)
- Massachusetts General Hospital(US)
- Sysmex (Germany)(DE)
- Paracelsus Medical University(AT)