Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

Ronzio, Luca; Cabitza, Federico; Barbaro, Alessandro; Banfi, Giuseppe

doi:10.3390/diagnostics11020372

Cited by 24 publications

(10 citation statements)

References 73 publications

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“…However, with the availability of huge amount of well-structured patient data in combination with the increased computational power, laboratory medicine is a perfect playing ground for the development of AI models. However, currently only the minority of FDA approved systems include laboratory diagnostics [17,42].…”

Section: Ai In the Medical Laboratorymentioning

confidence: 99%

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Cadamuro

2021

Diagnostics

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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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Section: Ai In the Medical Laboratorymentioning

confidence: 99%

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Cadamuro

2021

Diagnostics

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“…Three key ingredients for augmenting laboratory medicine have become available to researchers on a wider scale: learning and training algorithms, necessary computational power to run said algorithms, and high-volume data [ 2 ]. These latest and future developments of AI and ML in laboratory medicine, however, do not constitute the main focus of this experience-based opinion article, since several recently published reviews can offer an excellent overview [ 1 , 2 , 3 , 4 , 5 ]. We will, instead, highlight the principles required for high-quality, clinical, “big” data.…”

Section: Introductionmentioning

confidence: 99%

Big Data in Laboratory Medicine—FAIR Quality for AI?

et al. 2022

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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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“…Three key ingredients for augmenting laboratory medicine have become available to researchers on a wider scale: Learning and training algorithms, necessary computational power to run said algorithms, and high-volume data [2]. These latest and future developments of AI and ML in laboratory medicine, however, do not constitute the main focus of this experience-based opinion article, several recently published reviews can offer an excellent overview [1][2][3][4][5]. We will instead highlight the principles required for high quality, clinical, "big" data.…”

Section: Introductionmentioning

confidence: 99%

Big Data in Laboratory Medicine – FAIR Quality for AI?

Blatter¹,

Witte²,

Nakas³

et al. 2022

Preprint

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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 meta-data. 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 they 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.

show abstract

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

Cited by 24 publications

References 73 publications

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review

Big Data in Laboratory Medicine—FAIR Quality for AI?

Big Data in Laboratory Medicine – FAIR Quality for AI?

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