Explainable AI
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IMPORTANCE: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. OBJECTIVES: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. EXPOSURE: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. MAIN OUTCOMES AND MEASURES: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. RESULTS: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account.
- MeSH
- dospělí MeSH
- důvěra MeSH
- internacionalita MeSH
- lidé středního věku MeSH
- lidé MeSH
- nemocnice MeSH
- poskytování zdravotní péče * MeSH
- průřezové studie MeSH
- průzkumy a dotazníky MeSH
- senioři MeSH
- umělá inteligence * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.
- MeSH
- adenom * diagnostické zobrazování MeSH
- deep learning MeSH
- lidé MeSH
- nádory hypofýzy * diagnostické zobrazování MeSH
- počítačové zpracování obrazu metody MeSH
- radiomika MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
Digitalizace postupně proniká do velké části medicínských oblastí včetně patologie. Společně s digitálním zpracováním dat přichází aplikace metod umělé inteligence za účelem zjednodušení rutinních procesů, zvýšení bezpečnosti apod. Ačkoliv se obecné povědomí o metodách umělé inteligence zvyšuje, stále není pravidlem, že by odborníci z netechnických oborů měli detailní představu o tom, jak takové systémy fungují a jak se učí. Cílem tohoto textu je přístupnou formou vysvětlit základy strojového učení s využitím příkladů a ilustrací z oblasti digitální patologie. Nejedná se samozřejmě o ucelený přehled ani o představení nejmodernějších metod. Držíme se spíše úplných základů a představujeme fundamentální myšlenky, které stojí za většinou učících systémů, s použitím nejjednodušších modelů. V textu se věnujeme zejména rozhodovacím stromům, jejichž funkce je snadno vysvětlitelná, a elementárním neuronovým sítím, které jsou hlavním modelem používaným v dnešní umělé inteligenci. Pokusíme se také popsat postup spolupráce mezi lékaři, kteří dodávají data, a informatiky, kteří s jejich pomocí vytvářejí učící systémy. Věříme, že tento text pomůže překlenout rozdíly mezi znalostmi lékařů a informatiků a tím přispěje k efektivnější mezioborové spolupráci.
Digitalization has gradually made its way into many areas of medicine, including pathology. Along with digital data processing comes the application of artificial intelligence methods to simplify routine processes, enhance safety, etc. Although general awareness of artificial intelligence methods is increasing, it is still not common for professionals from non-technical fields to have a detailed understanding of how such systems work and learn. This text aims to explain the basics of machine learning in an accessible way using examples and illustrations from digital pathology. This is not intended to be a comprehensive overview or an introduction to cutting-edge methods. Instead, we use the simplest models to focus on fundamental concepts behind most learning systems. The text concentrates on decision trees, whose functionality is easy to explain, and basic neural networks, the primary models used in today’s artificial intelligence. We also attempt to describe the collaborative process between medical specialists, who provide the data, and computer scientists, who use this data to develop learning systems. This text will help bridge the knowledge gap between medical professionals and computer scientists, contributing to more effective interdisciplinary collaboration.
- MeSH
- lidé MeSH
- patologie * trendy MeSH
- strojové učení * trendy MeSH
- umělá inteligence trendy MeSH
- Check Tag
- lidé MeSH
During the first cell-fate decision of mouse preimplantation embryo development, a population of outer-residing polar cells is segregated from a second population of inner apolar cells to form two distinct cell lineages: the trophectoderm and the inner cell mass (ICM), respectively. Historically, two models have been proposed to explain how the initial differences between these two cell populations originate and ultimately define them as the two stated early blastocyst stage cell lineages. The 'positional' model proposes that cells acquire distinct fates based on differences in their relative position within the developing embryo, while the 'polarity' model proposes that the differences driving the lineage segregation arise as a consequence of the differential inheritance of factors, which exhibit polarized subcellular localizations, upon asymmetric cell divisions. Although these two models have traditionally been considered separately, a growing body of evidence, collected over recent years, suggests the existence of a large degree of compatibility. Accordingly, the main aim of this review is to summarize the major historical and more contemporarily identified events that define the first cell-fate decision and to place them in the context of both the originally proposed positional and polarity models, thus highlighting their functional complementarity in describing distinct aspects of the developmental programme underpinning the first cell-fate decision in mouse embryogenesis.
- MeSH
- biologické modely * MeSH
- buněčný rodokmen MeSH
- embryo savčí cytologie fyziologie MeSH
- embryonální vývoj fyziologie MeSH
- polarita buněk * MeSH
- signální transdukce MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
INTRODUCTION: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their "black box" structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. METHODS: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. RESULTS: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. CONCLUSION: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making.
- Publikační typ
- časopisecké články MeSH
The paper describes the concept of the Industry 4.0 and its reflection in health care. Industry 4.0 connects intelligent production concepts with external factors, including those linked with the production and those linked more with human, as for example intelligent homes or social web systems. Communication, data and information play an important role in the whole system. After explaining basic characteristics of the Industry 4.0 concept and its main parts, we show how they can be utilized in the health care sector and what their advantages are. Key technologies and techniques include Internet of Things, big data, artificial intelligence, data integration, robotization, virtual reality, and 3D printing. Finally, we identify the main challenges and research directions. Among the most important ones are interoperability, standardization, reliability, security and privacy, ethical and legal issues.
- MeSH
- big data MeSH
- lidé MeSH
- poskytování zdravotní péče * MeSH
- průmysl MeSH
- reprodukovatelnost výsledků MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
V článku je popsán vliv psychické zátěže na psychosomatické veličiny (srdeční frekvence, tlak krve, elektromyografické potenciály, kožní odpor). Psychologická zátěž byla vyvolána dvěma testy. První zátěžový test je zaměřen na zjištění úrovně verbální, percepční a numerické logiky, prostorové představivosti, technické a analytické schopnosti (testl). Jako druhý je použit tzv. sedmičkový test (test 2), který ovlivňuje pozornostní a paměťovou funkci při stresových podnětech. Klasifikace respondentů byla provedena: a) z psychologických dotazníků, b) z výsledků měření. Pro zpracování výsledků měření bylo použito logistické regresní analýzy, expertního systému, strojového učení a kombinace expertního systému se strojovým učením. Tato práce je úvodní, v následujících pracích budou jednotlivé metody podrobně rozpracovány.
The infiuence of a psychical load on psychosomatic quantities (heart beat frequency, blood pressure, electromyographic potentials, skin resistance) are described. The psychologie load is represented by two tests. The first one is intended for determination of verbal, perception a numerical logic level, 3D space imagination, and technical and analytical abilities (test 1). The second one is presented by the so-called sevens-test (test 2), which has an infiuence on the attention and memory functions during the time of stress. The classification was performed: a) using the psychological questionnaires, b) using the measurements results. Logistic regression analysis, expert system, machine learning and the combination of an expert systém and machine learning were used for the measurement results evaluation. This is an introductory the article, the individual methods will be explained in details in the following article.
Opakované, obtížně vysvětlitelné, popř. těžké hypoglykemie u pacientů s diabetes mellitus 1. typu (DM1T) mohou být vzácným, ale přesto reálným příznakem manifestace adrenální insuficience (AI). AI se objevuje u přibližně 0,5 % těchto pacientů, většinou několik let po vzniku diabetu, typicky ve středním věku a častěji u žen. Plošné vyšetřování pacientů trpících rekurentními, obvyklými příčinami nevysvětlitelnými hypoglykemickými epizodami na přítomnost AI se však neukázalo jako efektivní, a není tedy doporučováno. Přesto je nutné na AI jako na reálnou příčinu takových hypoglykemií u pacientů s DM1T pamatovat a prověřit u nich současnou přítomnost obvyklejších příznaků AI, kterými jsou především slabost, nechutenství, hubnutí, ortostatická hypotenze, či hyperpigmentace, případně poruchy mineralogramu (hyponatremie).
Recurrent, unexplained, eventually severe hypoglycemias in patients with type 1 diabetes mellitus (T1DM) may be rarely but yet associated with the onset of adrenal insufficiency (AI), present in approximately 0.5 % of patients with T1DM, appearing usually several years after diabetes diagnosis, usually in middle age and more frequently in women. Screening for AI in patients who complained of recurrent hypoglycemias, difficult to explain by common causes, was ineffective and therefore it is not recommended. Nevertheless, the possibility of manifestation of AI in connection with above mentioned cases in patients with T1DM should be taken into consideration and usual symptoms of AI such as weakness, lack of appetite, weight loss, orthostatic hypotension, hyperpigmentation or ions disorders should be examined.
- MeSH
- adrenální insuficience * komplikace MeSH
- diabetes mellitus MeSH
- hypoglykemie * etiologie MeSH
- komplikace diabetu etiologie MeSH
- lidé MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Ze starých epidemiologických populačních studií bylo známo, že koncentrace HDL-cholesterolu (HDL-C) byla v negativní asociaci s rizikem koronárních příhod. V posledních 10 letech se objevují studie, které objevují závislost tvaru křivky U, tj. nižší a vyšší hladiny HDL-C než optimální jsou v asociaci s exponenciálně se zvyšujícím rizikem. Asociace vysokých hladin HDL-C s vysokým kardiovaskulárním rizikem není zcela jasná. Předpokládá se změna funkce velkých HDL-částic bohatých na estery cholesterolu, které se stávají donorem cholesterolu pro arterie.
It is very well known from the old epidemiological population studies, that HDL-cholesterol (HDL-C) was in a negative association with coronary events risk. New studies have occurred during the last 10 years that the mentioned association has in fact the U-shaped curve, i.e. lower and higher levels of HDL-C then the optimal one are in an exponential increasing risk. The association of high HDL-C levels with cardiovascular risk is not explained clearly. There might be a hypothesis concerning a change of function of large HDL particles riched of cholesterol esters, which become to be cholesterol donors for the arteries.
- Klíčová slova
- kardiovaskulární riziko,
- MeSH
- apolipoprotein A-I krev MeSH
- biologické markery MeSH
- dospělí MeSH
- HDL-cholesterol * fyziologie krev škodlivé účinky MeSH
- kardiovaskulární nemoci * klasifikace mortalita patologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- lipoproteiny HDL MeSH
- rizikové faktory MeSH
- senioři MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- přehledy MeSH
