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

Analytical Performance Specifications for Input Variables: Investigation of the Model of End-Stage Liver Disease

BACKGROUND

Artificial intelligence models constitute specific uses of analysis results and, therefore, necessitate evaluation of analytical performance specifications (APS) for this context specifically. The Model of End-stage Liver Disease (MELD) is a clinical prediction model based on measurements of bilirubin, creatinine, and the international normalized ratio (INR). This study evaluates the propagation of error through the MELD, to inform choice of APS for the MELD input variables.

METHODS

A total of 6093 consecutive MELD scores and underlying analysis results were retrospectively collected. "Desirable analytical variation" based on biological variation as well as current local analytical variation was simulated onto the data set as well as onto a constructed data set, representing a worst-case scenario. Resulting changes in MELD score and risk classification were calculated.

RESULTS

Biological variation-based APS in the worst-case scenario resulted in 3.26% of scores changing by ≥1 MELD point. In the patient-derived data set, the same variation resulted in 0.92% of samples changing by ≥1 MELD point, and 5.5% of samples changing risk category. Local analytical performance resulted in lower reclassification rates.

CONCLUSIONS

Error propagation through MELD is complex and includes population-dependent mechanisms. Biological variation-derived APS were acceptable for all uses of the MELD score. Other combinations of APS can yield equally acceptable results. This analysis exemplifies how error propagation through artificial intelligence models can become highly complex. This complexity will necessitate that both model suppliers and clinical laboratories address analytical performance specifications for the specific use case, as these may differ from performance specifications for traditional use of the analyses.

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)

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.

Artificial Intelligence and laboratory data in rheumatic diseases

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.

Disruption vs. evolution in laboratory medicine. Current challenges and possible strategies, making laboratories and the laboratory specialist profession fit for the future

Since beginning of medical diagnostics, laboratory specialists have done an amazing job, continuously improving quality, spectrum and speed of laboratory tests, currently contributing to the majority of medical decision making. These improvements are mostly of an incremental evolutionary fashion, meaning improvements of current processes. Sometimes these evolutionary innovations are of a radical fashion, such as the invention of automated analyzers replacing manual testing or the implementation of mass spectrometry, leading to one big performance leap instead of several small ones. In few cases innovations may be of disruptive nature. In laboratory medicine this would be applicable to digitalization of medicine or the decoding of the human genetic material. Currently, laboratory medicine is again facing disruptive innovations or technologies, which need to be adapted to as soon as possible. One of the major disruptive technologies is the increasing availability and medical use of artificial intelligence. It is necessary to rethink the position of the laboratory specialist within healthcare settings and the added value he or she can provide to patient care. The future of the laboratory specialist profession is bright, as it the only medical profession comprising such vast experience in patient diagnostics. However, laboratory specialists need to develop strategies to provide this expertise, by adopting to the quickly evolving technologies and demands. This opinion paper summarizes some of the disruptive technologies as well as strategies to secure and/or improve the quality of diagnostic patient care and the laboratory specialist profession.

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories

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.

The preanalytical phase – from an instrument-centred to a patient-centred laboratory medicine

In order to guarantee patient safety, medical laboratories around the world strive to provide highest quality in the shortest amount of time. A major leap in quality improvement was achieved by aiming to avoid preanalytical errors within the total testing process. Although these errors were first described in the 1970s, it took additional years/decades for large-scale efforts, aiming to improve preanalytical quality by standardisation and/or harmonisation. Initially these initiatives were mostly on the local or national level. Aiming to fill this void, in 2011 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) working group “Preanalytical Phase” (WG-PRE) was founded. In the 11 years of its existence this group was able to provide several recommendations on various preanalytical topics. One major achievement of the WG-PRE was the development of an European consensus guideline on venous blood collection. In recent years the definition of the preanalytical phase has been extended, including laboratory test selection, thereby opening a huge field for improvement, by implementing strategies to overcome misuse of laboratory testing, ideally with the support of artificial intelligence models. In this narrative review, we discuss important aspects and milestones in the endeavour of preanalytical process improvement, which would not have been possible without the support of the Clinical Chemistry and Laboratory Medicine (CCLM) journal, which was one of the first scientific journals recognising the importance of the preanalytical phase and its impact on laboratory testing quality and ultimately patient safety.

Clinlabomics: leveraging clinical laboratory data by data mining strategies

The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.

A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories

OBJECTIVES

The Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills and technological predisposition in clinical laboratories.

METHODS

A questionnaire, focussing on digitization, information technology (IT) infrastructures, data accessibility, and BAI projects underway was sent to 1,351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets.

RESULTS

The 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%-including Laboratory Directors-cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient's health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the Laboratory Information System, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgements, indicating a growing interest.

CONCLUSIONS

The questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to all-around work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training.

Big Data in Laboratory Medicine—FAIR Quality for AI?

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.

Flowing through laboratory clinical data: the role of artificial intelligence and big data

During the last few years, clinical laboratories have faced a sea change, from facilities producing a high volume of low-cost test results, toward a more integrated and patient-centered service. Parallel to this paradigm change, the digitalization of healthcare data has made an enormous quantity of patients' data easily accessible, thus opening new scenarios for the utilization of artificial intelligence (AI) tools. Every day, clinical laboratories produce a huge amount of information, of which patients' results are only a part. The laboratory information system (LIS) may include other "relevant" compounding data, such as internal quality control or external quality assessment (EQA) results, as well as, for example, timing of test requests and of blood collection and exams transmission, these data having peculiar characteristics typical of big data, as volume, velocity, variety, and veracity, potentially being used to generate value in patients' care. Despite the increasing interest expressed in AI and big data in laboratory medicine, these topics are approaching the discipline slowly for several reasons, attributable to lack of knowledge and skills but also to poor or absent standardization, harmonization and problematic regulatory and ethical issues. Finally, it is important to bear in mind that the mathematical postulation of algorithms is not sufficient for obtaining useful clinical tools, especially when biological parameters are not evaluated in the appropriate context. It is therefore necessary to enhance cooperation between laboratory and AI experts, and to coordinate and govern processes, thus favoring the development of valuable clinical tools.

How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.

Artificial intelligence at the time of COVID-19: who does the lion's share?

OBJECTIVES

The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19.

METHODS

We conducted a PubMed search using as query MeSH major terms "Artificial Intelligence" AND "COVID-19", searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved.

RESULTS

Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models.

CONCLUSIONS

The lion's share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

OBJECTIVES

Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors.

METHODS

De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model's predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision.

RESULTS

Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%).

CONCLUSIONS

Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

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

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.