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In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns. This study delineates AI's dual role in scientific publishing: as a co-creator in the writing and review of scientific papers and as an ethical challenge. We first explore the potential of AI as an enhancer of efficiency, efficacy, and quality in creating scientific papers. A critical assessment follows, evaluating the risks vs. rewards for researchers, especially those early in their careers, emphasizing the need to maintain a balance between AI's capabilities and fostering independent reasoning and creativity. Subsequently, we delve into the ethical dilemmas of AI's involvement, particularly concerning originality, plagiarism, and preserving the genuine essence of scientific discourse. The evolving dynamics further highlight an overlooked aspect: the inadequate recognition of human reviewers in the academic community. With the increasing volume of scientific literature, tangible metrics and incentives for reviewers are proposed as essential to ensure a balanced academic environment. AI's incorporation in scientific publishing is promising yet comes with significant ethical and operational challenges. The role of human reviewers is accentuated, ensuring authenticity in an AI-influenced environment. As the scientific community treads the path of AI integration, a balanced symbiosis between AI's efficiency and human discernment is pivotal. Emphasizing human expertise, while exploit artificial intelligence responsibly, will determine the trajectory of an ethically sound and efficient AI-augmented future in scientific publishing. [ABSTRACT FROM AUTHOR]

The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Laboratories and diagnostic departments are presiding over a massive amount of data they are failing to fully leverage it. Data is the new black gold of healthcare organizations and by extracting insights from it, laboratories could become true decision engines, able to drive action across healthcare. This opinion paper responds three fundamental questions: (1) Where are we (diagnostic parties)? Taking a look at the most significant trends and challenges in healthcare and shedding some light upon the status of diagnostics. (2) Where do we want to be? Reviewing the opportunities for digital health, its role in the healthcare of the future and providing inspiration about what success looks like. (3) What do we need to do? Explaining what Digital Health Solutions (DHS) from Abbott is doing in this regard. This will include information about how DHS can impact the Diagnosis Cycle and how to set a roadmap for laboratories and diagnostic organizations. Diagnosis Cycle means the different steps in the diagnosis process, from the beginning when a patient is seen by a clinician and some tests are ordered, until the results are reviewed by the clinician and the treatment, follow up or discharge is decided. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As a result, the laboratory medical profession may now be facing a big transformation due to disruptive technologies, namely digitalization, Big Data, AI and machine learning (ML). First and foremost, however, clinical laboratories must ensure that laboratory data are accurate and reliable to avoid the risk of sophisticated systems such as ML and AI using inaccurate results which in turn can lead to inaccurate and potentially harmful information. Keywords: artificial intelligence; big data; clinical laboratories; laboratory medicine; machine learning EN artificial intelligence big data clinical laboratories laboratory medicine machine learning 1859 1861 3 11/07/22 20221101 NES 221101 The term Artificial Intelligence (AI) was originally used by John McCarthy at the Dartmouth conference in 1956 and was defined as the "theory and development of computer systems which perform tasks that normally require human intelligence" [[1]]. [Extracted from the article]

A number of current trends will affect and probably change laboratory medicine, as we know it. Scientific and technological developments, digital health with big data and artificial intelligence, and centralization will change the interfaces among the specialties of laboratory medicine. They might even challenge the identity of some specialties. Other trends such as demographic changes, increased complexity of health care, digital health with electronic health records, and more demanding and well-informed patients will change the way laboratory medicine specialties deliver their services. This paper discusses the possible changes of laboratory medicine in Denmark – a Scandinavian country where almost all hospitals are public. If Danish laboratories grasp the new possibilities instead of trying to avoid them, laboratory medicine is likely to prosper. Such a positive development will call upon good leadership and a genuine willingness among laboratory specialist to adapt to a future where their own specialty might be very different from today. [ABSTRACT FROM AUTHOR]

The article discusses the application of artificial intelligence (AI) in the pre-analytical phase of laboratory testing. The authors reflect on the methodological aspects of using AI to infer the range of assay results based on ancillary information from the Laboratory Information System. They compare the use of artificial neural networks (ANN) and decision trees (DT) in this context, noting that an ANN may be more suitable due to its ability to learn continuously. The authors also discuss the economic implications of using AI in terms of cost savings and the impact on turnaround time. Lastly, they highlight the ethical considerations of using AI to improve patient safety and the potential for AI to enhance automation in the laboratory system. [Extracted from the article]

The letter discusses the accuracy of the automated chat tool Chatbot GPT, which uses artificial intelligence to answer questions. The author submitted two questions to the chatbot and found that while the output was mostly accurate, there were some inaccuracies in the information provided. In one case, the chatbot attributed advancements in prostate specific antigen assays to the author, when in fact they were made by other scientists. In another case, the chatbot credited the author with achievements in space exploration that were actually made by someone else with a similar name. The author concludes that Chatbot GPT should be used with caution due to its potential for inaccuracy and misleading information. [Extracted from the article]

The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG. A deep convolutional neural network was developed using the population-based cohort study Hamburg City Health Study (HCHS, n=8,253, 50.9 % women). External validation was performed in two independent population-based cohorts (SHIP-START, n=3,002, 52.1 % women, and SHIP-TREND, n=3,819, 51.2 % women). Assessment of model performance was conducted using Pearson correlation (R) and area under the receiver operating characteristics curve (AUROC). NT-proBNP was predictable from the ECG (R, 0.566 [HCHS], 0.642 [SHIP-START-0], 0.655 [SHIP-TREND-0]). Across cohorts, predicted NT-proBNP (pNT-proBNP) showed good discriminatory ability for prevalent and incident heart failure (HF) (baseline: AUROC 0.795 [HCHS], 0.816 [SHIP-START-0], 0.783 [SHIP-TREND-0]; first follow-up: 0.669 [SHIP-START-1, 5 years], 0.689 [SHIP-TREND-1, 7.3 years]), comparable to the discriminatory value of measured NT-proBNP. pNT-proBNP also demonstrated comparable results for other incident CVD, including atrial fibrillation, stroke, myocardial infarction, and cardiovascular death. Deep learning ECG algorithms can predict NT-proBNP concentrations with high diagnostic and predictive value for HF and other major CVD and may be used in the community to identify individuals at risk. Long-standing experience with NT-proBNP can increase acceptance of such deep learning models in clinical practice. [ABSTRACT FROM AUTHOR]

Interpretation of laboratory data is a comparative procedure and requires reliable reference data, which are mostly derived from population data but used for individuals in conventional laboratory medicine. Using population data as a "reference" for individuals has generated several problems related to diagnosing, monitoring, and treating single individuals. This issue can be resolved by using data from individuals' repeated samples, as their personal reference, thus needing that laboratory data be personalized. The modern laboratory information system (LIS) can store the results of repeated measurements from millions of individuals. These data can then be analyzed to generate a variety of personalized reference data sets for numerous comparisons. In this manuscript, we redefine the term "personalized laboratory medicine" as the practices based on individual-specific samples and data. These reflect their unique biological characteristics, encompassing omics data, clinical chemistry, endocrinology, hematology, coagulation, and within-person biological variation of all laboratory data. It also includes information about individuals' health behavior, chronotypes, and all statistical algorithms used to make precise decisions. This approach facilitates more accurate diagnosis, monitoring, and treatment of diseases for each individual. Furthermore, we explore recent advancements and future challenges of personalized laboratory medicine in the context of the digital health era. [ABSTRACT FROM AUTHOR]

In the digital age, the metaverse has emerged with impressive potential for many segments of society. The metaverse could be presented as a parallel dimension able to enhance the physical world as well as our actions and decisions in it with the objective to use a coalition between the natural and virtual worlds for value creation. Our aim was to elaborate on the impact of the metaverse on laboratory medicine. Based on the available evidence, literature and reports, we analyzed the different perspectives of the metaverse on laboratory medicine and the needs for an efficient transition. The convergence and integration of technologies in the metaverse will participate to the reimagination of laboratory medicine services with augmented services, users' experiences, efficiency, and personalized care. The revolution around the metaverse offers different opportunities for laboratory medicine but also open multiple related challenges that are presented in this article. Scientific societies, multidisciplinary teams and specialists in laboratory medicine must prepare the integration metaverse and meta-medical laboratories, raise the awareness, educate, set guidance to obtain a maximum of value and mitigate potential adverse consequences. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Human pathologies are complex and might benefit from a more holistic diagnostic approach than currently practiced. Omics is a concept in biological research that aims to comprehensively characterize and quantify large numbers of biological molecules in complex samples, e.g., proteins (proteomics), low molecular weight molecules (metabolomics), glycans (glycomics) or amphiphilic molecules (lipidomics). Over the past decades, respective unbiased discovery approaches have been intensively applied to investigate functional physiological and pathophysiological relationships in various research study cohorts. In the context of clinical diagnostics, omics approaches seem to have potential in two main areas: (i) biomarker discovery i.e. identification of individual marker analytes for subsequent translation into diagnostics (as classical target analyses with conventional laboratory techniques), and (ii) the readout of complex, higher-dimensional signatures of diagnostic samples, in particular by means of spectrometric techniques in combination with biomathematical approaches of pattern recognition and artificial intelligence for diagnostic classification. Resulting diagnostic methods could potentially represent a disruptive paradigm shift away from current one-dimensional (i.e., single analyte marker based) laboratory diagnostics. The underlying hypothesis of omics approaches for diagnostics is that complex, multigenic pathologies can be more accurately diagnosed via the readout of "omics-type signatures" than with the current one-dimensional single marker diagnostic procedures. While this is indeed promising, one must realize that the clinical translation of high-dimensional analytical procedures into routine diagnostics brings completely new challenges with respect to long-term reproducibility and analytical standardization, data management, and quality assurance. In this article, the conceivable opportunities and challenges of omics-based laboratory diagnostics are discussed. [ABSTRACT FROM AUTHOR]

Artificial intelligence plays an important role in contemporary medicine. In this short note, we emphasize that philosophy played a role in the development of artificial intelligence. We argue that research in computability theory, the theoretical foundation of modern computer science, was motivated by a philosophical question: can we characterize precisely the class of problems that can be solved by algorithms? We suggest that reflecting on the connection between philosophy and artificial intelligence helps realize that philosophical and scientific progress are connected. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR). An analytical profile consisting of 25 predefined routine laboratory blood tests was introduced to general practitioners (GPs) to be used for patients with non-specific symptoms, as an additional tool to identify individuals at increased risk of cancer. Consecutive analytical profiles ordered by GPs from November 29th 2011 until March 1st 2020 were included. AI and LR analysis were performed on data from 6,592 analytical profiles for their ability to detect cancer. Cohort I for model development included 5,224 analytical profiles ordered by GP's from November 29th 2011 until the December 31st 2018, while 1,368 analytical profiles included from January 1st 2019 until March 1st 2020 constituted the "out of time" validation test Cohort II. The main outcome measure was a cancer diagnosis within 90 days. The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90 days. Results obtained with the AI model were comparable to results from the LR model. In the internal validation Cohort IB, the AI model provided slightly better results than the LR analysis both in terms of the area under the receiver operating characteristics curve (AUC) and PPV, sensitivity/specificity while in the "out of time" validation test Cohort II, the obtained results were comparable. The AI risk score may be a valuable tool in the clinical decision-making. The score should be further validated to determine its applicability in other populations. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Healthcare providers all over the world are faced with a single challenge: the need to improve patient outcomes while containing costs. Drivers include an increasing demand for chronic disease management for an aging population, technological advancements and empowered patients taking control of their health experience. The digital transformation in healthcare, through the creation of a rich health data foundation and integration of technologies like the Internet of Things (IoT), advanced analytics, Machine Learning (ML) and Artificial Intelligence (AI), is recognized as a key component to tackle these challenges. It can lead to improvements in diagnostics, prevention and patient therapy, ultimately empowering care givers to use an evidence-based approach to improve clinical decisions. Real-time interactions allow a physician to monitor a patient 'live', instead of interactions once every few weeks. Operational intelligence ensures efficient utilization of healthcare resources and services provided, thereby optimizing costs. However, procedure-based payments, legacy systems, disparate data sources with the limited adoption of data standards, technical debt, data security and privacy concerns impede the efficient usage of health information to maximize value creation for all healthcare stakeholders. This has led to a highly-regulated, constrained industry. Ultimately, the goal is to improve quality of life and saving people's lives through the creation of the intelligent healthcare provider, fully enabled to deliver value-based healthcare and a seamless patient experience. Information technologies that enable this goal must be extensible, safe, reliable and affordable, and tailored to the digitalization maturity-level of the individual organization. [ABSTRACT FROM AUTHOR]

As automation and electronics have already achieved the most significant improvements in automation, the only possible way seems to be to enhance their use through IT capabilities by adopting the so-called artificial intelligence (AI). To envision how laboratory automation could evolve under the effect of AI implementation, we can take a look at the manufacturing industry, where all this is already happening [[6]]. [Extracted from the article]

This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computerbased support in pathology and laboratory medicine. [ABSTRACT FROM AUTHOR]

Background: Automated systems have been broadly used in the counting of particles in urine, while manual microscopic analyses are still required for confirming components of urine sediments, especially pathologic casts and other unknown particles. Good review rules can reduce the number of manual urine microscopy examinations safely, thereby increasing productivity. Although several methods have been proposed, establishment of microscopic review rules for flow cytometer remains challenging. Methods: A total of 3014 urine samples from outpatient and inpatient were examined using UF-1000i flow cytometry, Urisys-2400 dipstick and RS 2003 urine sediment workstation, respectively. Based on the results above, three supervised machine learning methods were employed to construct classifiers for screening urine samples. Results: Here, we propose a novel method for construction of microscopic review rules, termed UrineCART, which was based on a classification and regression tree (CART) method. With a cut-off value of 0.0745 for UrineCART, we obtained a sensitivity of 92.0%, a specificity of 81.5% and a total review rate of 32.4% on an independent test set. Comparisons with the existing methods showed that UrineCART gave the acceptable sensitivity and lower total review rate. Conclusions: An algorithm based on machine learning methods for review criteria can be achieved via systematic comparison of UF-1000i flow cytometry and microscopy. Using UrineCART, our microscopic review rate can be reduced to around 30%, while decreasing significant losses in urinalysis. [ABSTRACT FROM AUTHOR]

Background: Back propagation (BP) artificial neural networks are a distribution-free method for data analysis based on layers of artificial neurons that transduce imputed information. It has been recognized as having a number of advantages compared to traditional methods including the possibility to process imperfect data, and complex non-linear data. The objective of this study was to review the principles, procedures, and limitations of BP artificial neural networks for a non-mathematical readership. Methods: A real data sample of weight, height and measured body surface area from 90 individuals was used as an example. SPSS 17.0 with neural network add-on was used for the analysis. The predicted body surface from a two hidden layer BP neural network was compared to the body surface calculated by the Haycock equation. Results: Both the predicted values from the neural network and from the Haycock equation were close to the measured values. A linear regression analysis with neural network as predictor produced an r2-value of 0.983, while the Haycock equation produced an r2-value of 0.995 (r2>0.95 is a criterion for accurate diagnostic testing). Conclusions: BP neural networks may, sometimes, predict clinical diagnoses with accuracies similar to those of other methods. However, traditional statistical procedures, such as regression analyses need to be added for testing their accuracies against alternative methods. Nonetheless, BP neural networks have great potential through their ability to learn by example instead of learning by theory. Clin Chem Lab Med 2010;48:159–65. [ABSTRACT FROM AUTHOR]

Background: To investigate the potential use of Artificial Neural Network (ANN) in the evaluation of serum protein electrophoresis, we set up a multicenter study involving six Italian laboratories. For this purpose, we developed an algorithm named CASPER (Computer Assisted Serum Protein Electrophoresis Recognizer). Methods: A total of 59,516 samples from the six centers were divided into three groups. Training and validation sets were used to develop the neural network, whereas evaluation set was used to test the performance of CASPER in recognizing abnormal electrophoretic profiles. Results: CASPER showed 93.0% sensitivity and 47.4% specificity. CASPER sensitivity and specificity ranged in the six sites from 88% (site 3) to 97% (site 5) and from 36% (site 6) to 53% (site 3), respectively. Sensitivity for γ zone was 94.6%, for β zone 89.7% and for oligoclonal patterns 92.0%. Conclusions: The sensitivity of the CASPER algorithm does not allow us to recommend its use as a replacement for the visual inspection, but it could be helpful in avoiding accidental misclassifications by the operator. Moreover, the CASPER algorithm may be a useful tool for training operators and students. This study evidenced a high inter-observer variability, which should be addressed in a dedicated study. Data set to train and validate ANNs should contain a huge range and an adequate number of different abnormalities. Clin Chem Lab Med 2008;46:1183–8. [ABSTRACT FROM AUTHOR]