Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.
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- biopsie MeSH
- hyperplazie MeSH
- lidé MeSH
- počítače * MeSH
- reprodukovatelnost výsledků MeSH
- studie proveditelnosti MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
PURPOSE OF REVIEW: The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice. RECENT FINDINGS: In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.
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- biologické markery MeSH
- lidé MeSH
- lokální recidiva nádoru genetika MeSH
- nádory močového měchýře * diagnóza genetika terapie MeSH
- prostata * MeSH
- recidiva MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
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- inteligence MeSH
- kybernetika MeSH
- lidé MeSH
- modely neurologické MeSH
- mozek fyziologie MeSH
- nervová síť MeSH
- neuronové sítě (počítačové) MeSH
- pud MeSH
- robotika MeSH
- umělá inteligence * MeSH
- vědomí MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
- MeSH
- biomimetika MeSH
- filozofie MeSH
- informační systémy MeSH
- lidé MeSH
- neuronové sítě (počítačové) MeSH
- počítačové systémy MeSH
- průmysl MeSH
- robotika MeSH
- strojové učení MeSH
- systémy člověk-stroj MeSH
- teoretické modely MeSH
- umělá inteligence * MeSH
- vědomí MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
- MeSH
- kognice MeSH
- lidé MeSH
- metafyzické vztahy mezi duší a tělem MeSH
- náboženství a psychologie MeSH
- sociální změna MeSH
- spiritualita MeSH
- umělá inteligence MeSH
- vědomí * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Background: Femoroacetabular impingement syndrome (FAI) is a complex, often post-traumatically developing impairment of the hip joint. It is characterized by ambiguous symptomatology, which makes early diagnosis difficult. Aim: The study was conducted to evaluate the applicability of a triaxial gyroscopic sensor in routine practice as an additional indication criterion for operative versus conservative treatment procedures. Methods: Ninety-two patients were included in the experimental retrospective study and 62 completed the examination. All patients signed informed consent. A gyroscopic sensor was placed on the right side of the pelvis above the hip joint and patients walked approximately 15 steps. Data were also evaluated while the patients climbed stairs. A complete clinical examination of the dynamics and physiological movements in the joint was performed. The data measured by the gyroscopic sensor were processed using differential geometry methods and subsequently evaluated using spectral analysis and neural networks. Results: FAI diagnosis using gyroscopic measurement is fast and easy to implement. Our approach to processing the gyroscopic signals used to detect the stage of osteoarthritis and post-traumatic FAI could lead to more accurate detection and capture early in FAI development. Conclusions: The obtained data are easily evaluated, interpretable, and beneficial in the diagnosis of the early stages of FAI. The results of the study show that this approach can lead to more accurate and early detection of osteoarthritis and post-traumatic FAI.
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- analýza chůze metody MeSH
- biomechanika * MeSH
- femoroacetabulární impingement * chirurgie diagnóza patofyziologie MeSH
- kyčelní kloub patofyziologie MeSH
- lidé MeSH
- nositelná elektronika * MeSH
- osteoartróza diagnóza patofyziologie MeSH
- retrospektivní studie MeSH
- telemedicína metody MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
AIMS: The aim of this study was to compare the clinical decision-making for benzodiazepine deprescribing between a healthcare provider (HCP) and an artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). METHODS: We analysed real-world data from a Croatian cohort of community-dwelling benzodiazepine patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the data using pre-established deprescribing criteria to assess benzodiazepine discontinuation potential. The research team devised and tested AI prompts to ensure consistency with HCP judgements. An independent researcher employed ChatGPT-4 with predetermined prompts to simulate clinical decisions for each patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared for agreement rates and Cohen's kappa. RESULTS: Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (κ = .200, P = .012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3% (lack of indication κ = .352, P < .001; prolonged use κ = .088, P = .280; safety concerns κ = .123, P = .006; incorrect dosage κ = .264, P = .001). Important limitations of GPT-4 responses were identified, including 22.1% ambiguous outputs, generic answers and inaccuracies, posing inappropriate decision-making risks. CONCLUSIONS: While AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable clinical decision-making. This study's findings reveal both strengths and areas for enhancement of ChatGPT-4 in the deprescribing recommendations within a real-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advancement of AI for optimal performance.
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- benzodiazepiny škodlivé účinky MeSH
- depreskripce * MeSH
- klinické rozhodování MeSH
- lidé MeSH
- umělá inteligence * MeSH
- zdravotnický personál MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.
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- dialýza ledvin MeSH
- lidé MeSH
- nefrologie * MeSH
- nefrologové MeSH
- průzkumy a dotazníky MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
