PURPOSE OF REVIEW: A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors. RECENT FINDINGS: Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services. SUMMARY: In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

• MeSH
• klinické laboratoře MeSH
• lidé MeSH
• průzkumy a dotazníky MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

• MeSH
• algoritmy MeSH
• databáze genetické MeSH
• fenotyp * MeSH
• genomika * metody   MeSH
• lidé MeSH
• software * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Optimal management of outpatients with heart failure (HF) requires serially updating the estimates of their risk for adverse clinical outcomes to guide treatment. Patient-reported outcomes (PROs) are becoming increasingly used in clinical care. The purpose of this study was to determine whether the inclusion of PROs can improve the risk prediction for HF hospitalization and death in ambulatory patients with HF. METHODS AND RESULTS: We included consecutive patients with HF with reduced ejection fraction (HFrEF) and HF with preserved EF (HFpEF) seen in a HF clinic between 2015 and 2019 who completed PROs as part of routine care. Cox regression with a least absolute shrinkage and selection operator regularization and gradient boosting machine analyses were used to estimate risk for a combined outcome of HF hospitalization, heart transplant, left ventricular assist device implantation, or death. The performance of the prediction models was evaluated with the time-dependent concordance index (Cτ). Among 1165 patients with HFrEF (mean age 59.1 ± 16.1, 68% male), the median follow-up was 487 days. Among 456 patients with HFpEF (mean age 64.2 ± 16.0 years, 55% male) the median follow-up was 494 days. Gradient boosting regression that included PROs had the best prediction performance - Cτ 0.73 for patients with HFrEF and 0.74 in patients with HFpEF, and showed very good stratification of risk by time to event analysis by quintile of risk. The Kansas City Cardiomyopathy Questionnaire overall summary score, visual analogue scale and Patient Reported Outcomes Measurement Information System dimensions of satisfaction with social roles and physical function had high variable importance measure in the models. CONCLUSIONS: PROs improve risk prediction in both HFrEF and HFpEF, independent of traditional clinical factors. Routine assessment of PROs and leveraging the comprehensive data in the electronic health record in routine clinical care could help more accurately assess risk and support the intensification of treatment in patients with HF.

• MeSH
• hodnocení rizik metody   MeSH
• hodnocení výsledků péče pacientem * MeSH
• hospitalizace statistika a číselné údaje   MeSH
• kvalita života * psychologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• následné studie MeSH
• retrospektivní studie MeSH
• senioři MeSH
• srdeční selhání * patofyziologie   psychologie   terapie   diagnóza   mortalita   MeSH
• tepový objem fyziologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

The formation of memories is a complex, multi-scale phenomenon, especially when it involves integration of information from various brain systems. We have investigated the differences between a novel and consolidated association of spatial cues and amphetamine administration, using an in situ hybridisation method to track the short-term dynamics during the recall testing. We have found that remote recall group involves smaller, but more consolidated groups of neurons, which is consistent with their specialisation. By employing machine learning analysis, we have shown this pattern is especially pronounced in the VTA; furthermore, we also uncovered significant activity patterns in retrosplenial and prefrontal cortices, as well as in the DG and CA3 subfields of the hippocampus. The behavioural propensity towards the associated localisation appears to be driven by the nucleus accumbens, however, further modulated by a trio of the amygdala, VTA and hippocampus, as the trained association is confronted with test experience. Moreover, chemogenetic analysis revealed central amygdala as critical for linking appetitive emotional states with spatial contexts. These results show that memory mechanisms must be modelled considering individual differences in motivation, as well as covering dynamics of the process.

• MeSH
• amfetamin farmakologie   MeSH
• amygdala fyziologie   MeSH
• hipokampus * fyziologie   MeSH
• konsolidace paměti * fyziologie   MeSH
• krysa rodu rattus MeSH
• mozek fyziologie   MeSH
• neurony fyziologie   metabolismus   MeSH
• nucleus accumbens * fyziologie   MeSH
• odměna * MeSH
• paměť fyziologie   MeSH
• podněty MeSH
• prefrontální mozková kůra fyziologie   MeSH
• rozpomínání * fyziologie   MeSH
• strojové učení MeSH
• tegmentum mesencephali - area ventralis * fyziologie   MeSH
• zvířata MeSH
• Check Tag
• krysa rodu rattus MeSH
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.

• MeSH
• feochromocytom * diagnostické zobrazování   MeSH
• lidé MeSH
• nádory nadledvin * diagnostické zobrazování   MeSH
• paragangliom * diagnostické zobrazování   MeSH
• počítačová rentgenová tomografie metody   MeSH
• počítačové zpracování obrazu metody   MeSH
• radiomika MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

Publikace se zaměřuje na zákony a trestní odpovědnost související s činností umělé inteligence. Určeno odborné veřejnosti.; Rozbor české trestněprávní úpravy postihující využití umělé inteligence jako nástroje trestné činnosti podle stavu k 30. 6. 2025.

• MeSH
• právní odpovědnost MeSH
• trestní zákon MeSH
• umělá inteligence zákonodárství a právo   MeSH
• zákonodárství jako téma MeSH
• Publikační typ
• monografie MeSH
• Geografické názvy
• Česká republika MeSH

• Konspekt
• Právo
• NLK Obory
• právo, zákonodárství

S postupující digitalizací patologie se do popředí zájmu dostávají i aplikace metod strojového učení a umělé inteligence. Výzkum a vývoj v této oblasti je velmi rychlý, ale aplikace učících systémů v klinické praxi stále zaostávají. Cílem tohoto textu je přiblížit proces tvorby a nasazení učících systémů v digitální patologii. Začneme popisem základních vlastností dat produkovaných v rámci digitální patologie. Konkrétně pojednáme o skenerech a skenování vzorků, o ukládání a přenosu dat, o kontrole jejich kvality a přípravě pro zpracování pomocí učících systémů, zejména o anotacích. Naším cílem je prezentovat aktuální přístupy k řešení technických problémů a zároveň upozornit na úskalí, na která lze narazit při zpracování dat z digitální patologie. V první části také naznačíme, jak vypadají aktuální softwarová řešení pro prohlížení naskenovaných vzorků a implementace diagnostických postupů zahrnujících učící systémy. Ve druhé části textu popíšeme obvyklé úlohy digitální patologie a naznačíme obvyklé přístupy k jejich řešení. V této části zejména vysvětlíme, jak je nutné modifikovat standardní metody strojového učení pro zpracování velkých skenů a pojednáme o konkrétních aplikacích v diagnostice. Na závěr textu poskytneme rychlý náhled dalšího možného vývoje učících systémů v digitální patologii. Zejména ilustrujeme podstatu přechodu na velké základní modely a naznačíme problematiku virtuálního barvení vzorků. Doufáme, že tento text přispěje k lepší orientaci v rapidně se vyvíjející oblasti strojového učení v digitální patologii a tím přispěje k rychlejší adopci učících metod v této oblasti.

With the advancing digitalization of pathology, the application of machine learning and artificial intelligence methods is becoming increasingly important. Research and development in this field are progressing rapidly, but the clinical implementation of learning systems still lags behind. The aim of this text is to provide an overview of the process of developing and deploying learning systems in digital pathology. We begin by describing the fundamental characteristics of data produced in digital pathology. Specifically, we discuss scanners and sample scanning, data storage and transmission, quality control, and preparation for processing by learning systems, with a particular focus on annotations. Our goal is to present current approaches to addressing technical challenges while also highlighting potential pitfalls in processing digital pathology data. In the first part of the text, we also outline existing software solutions for viewing scanned samples and implementing diagnostic procedures that incorporate learning systems. In the second part of the text, we describe common tasks in digital pathology and outline typical approaches to solving them. Here, we explain the necessary modifications to standard machine learning methods for processing large scans and discuss specific diagnostic applications. Finally, we provide a brief overview of the potential future development of learning systems in digital pathology. We illustrate the transition to large foundational models and introduce the topic of virtual staining of samples. We hope that this text will contribute to a better understanding of the rapidly evolving field of machine learning in digital pathology and, in turn, facilitate the faster adoption of learning-based methods in this domain.

Práce se zabývá využitím algoritmů umělé inteligence (artificial intelligence – AI) v diagnostice karcinomu prsu, plic a prostaty. Popisuje historický vývoj digitalizace patologických procesů, implementaci umělé inteligence a její současné aplikace v patologii. Zaměřuje se na strojové a hluboké učení, počítačové vidění a digitální patologii, které přispívají k automatizaci a zpřesnění diagnostiky. Důraz je kladen na konkrétní nástroje, jako jsou systémy uPath od Roche a IBEX Medical Analytics, které umožňují analýzu histopatologických snímků, klasifikaci nádorových buněk a hodnocení biomarkerů. Práce také reflektuje výhody využití AI, včetně zvýšení přesnosti diagnostiky a efektivity laboratorních procesů, ale zároveň upozorňuje na výzvy spojené s její implementací, jako jsou etické a právní aspekty, ochrana osobních údajů a odpovědnost za chyby. Cílem práce je poskytnout komplexní přehled o možnostech využití AI v digitální patologii a její roli v moderní onkologické diagnostice.

The study focuses on the utilization of artificial intelligence (AI) algorithms in the diagnosis of breast, lung, and prostate cancer. It describes the historical development of the digitalization of pathological processes, the implementation of artificial intelligence, and its current applications in pathology. The study emphasizes machine learning, deep learning, computer vision, and digital pathology, which contribute to the automation and refinement of diagnostics. Special attention is given to specific tools such as the uPath systems from Roche and IBEX Medical Analytics, which enable the analysis of histopathological images, tumor cell classification, and biomarker evaluation. The study also highlights the benefits of AI utilization, including increased diagnostic accuracy and efficiency in laboratory processes, while simultaneously addressing the challenges associated with its implementation, such as ethical and legal considerations, data protection, and liability for errors. The aim of this study is to provide a comprehensive overview of the potential applications of AI in digital pathology and its role in modern oncological diagnostics.

• Klíčová slova
• systém Genius,
• MeSH
• časná detekce nádoru metody   přístrojové vybavení   MeSH
• digitální technologie klasifikace   metody   MeSH
• lidé MeSH
• nádory děložního čípku diagnostické zobrazování   diagnóza   prevence a kontrola   MeSH
• Papanicolaouův test * metody   přístrojové vybavení   MeSH
• počítačové zpracování obrazu metody   přístrojové vybavení   MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• přehledy MeSH
• Geografické názvy
• Česká republika MeSH