The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.
- MeSH
- Alzheimer Disease * diagnosis drug therapy metabolism MeSH
- Clinical Trials as Topic MeSH
- Humans MeSH
- Neuroimaging methods MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Nowadays, artificial intelligence (AI) affects our lives every single day and brings with it both benefits and risks for all spheres of human activities, including education. Out of these risks, the most striking seems to be ethical issues of the use of AI, such as misuse of private data or surveillance of people's lives. Therefore, the aim of this systematic review is to describe the key ethical issues related to the use of AI-driven mobile apps in education, as well as to list some of the implications based on the identified studies associated with this research topic. The methodology of this review study was based on the PRISMA guidelines for systematic reviews and meta-analyses. The results indicate four key ethical principles that should be followed, out of which the principle of algorithmovigilance should be considered in order to monitor, understand and prevent the adverse effects of algorithms in the use of AI in education. Furthermore, all stakeholders should be identified, as well as their joint engagement and collaboration to guarantee the ethical use of AI in education. Thus, the contribution of this study consists in emphasizing the need for joint cooperation and research of all stakeholders when using AI-driven mobile technologies in education with special attention to the ethical issues since the present research based on the review studies is scarce and neglected in this respect.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Mobile Applications * MeSH
- Educational Status MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- Systematic Review MeSH
BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features. RESULTS: The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class). DISCUSSION: Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.
In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.
- MeSH
- Algorithms MeSH
- Data Mining methods MeSH
- Databases, Protein MeSH
- Humans MeSH
- Proteins chemistry MeSH
- Reproducibility of Results MeSH
- Structural Homology, Protein MeSH
- Protein Structure, Tertiary MeSH
- Artificial Intelligence MeSH
- Computational Biology methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class 'Artificial Intelligence in Medicine Challenge', which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 'AF Classification from a Short Single Lead ECG Recording'. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge.
Cíl: Práce seznamuje čtenáře s pokroky v hodnocení snímků sítnice pomocí umělé inteligence se zaměřením na screening diabetické retinopatie (DR). Popsány budou základní principy umělé inteligence a algoritmy, které se již dnes v klinické praxi používají nebo jsou krátce před schválením. Metodika: Literární rešerše zaměřená na charakteristiky a mechanismy jednotlivých přístupů k využití umělé inteligence (artificial intelligence AI). Hodnotili jsme anglicky psané články publikované do června 2020 s užitím klíčových slov: „diabetic retinopathy screening“, „deep learning“, „artificial intelligence“ a „automated diabetic retinopathy system“. Výsledky: Moderní systémy pro screening diabetické retinopatie využívající hluboké neuronové sítě dosahují ve většině publikovaných studií senzitivity i specificity nad 80 %. Výsledky konkrétních studií se liší v závislosti na definici zlatého standardu, velikosti souboru a hodnocených parametrech. Závěr: Hodnocení snímků pomocí AI do budoucna zrychlí a zefektivní diagnostiku DR a umožní i při nárůstu pacientů s diabetem bez adekvátního nárůstu počtu oftalmologů zachovat minimálně stávající kvalitu péče.
Objective: The aim of this comprehensive paper is to acquaint the readers with evaluation of the retinal images using the arteficial intelligence (AI). Main focus of the paper is diabetic retinophaty (DR) screening. The basic principles of the artificial intelligence and algorithms that are already used in clinical practice or are shortly before approval will be described. Methodology: Describing the basic characteristics and mechanisms of different approaches to the use of AI and subsequently literary minireview clarifying the current state of knowledge in the area. Results: Modern systems for screening diabetic retinopathy using deep neural networks achieve a sensitivity and specificity of over 80 % in most published studies. The results of specific studies vary depending on the definition of the gold standard, number of images tested and on the evaluated parameters. Conclusion: Evaluation of images using AI will speed up and streamline the diagnosis of DR. The use of AI will allow to keep the quality of the eye care at least on the same level despite the raising number of the patients with diabetes.
- MeSH
- Algorithms MeSH
- Diabetic Retinopathy * diagnosis MeSH
- Humans MeSH
- Sensitivity and Specificity MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
Umělá inteligence (AI) se stále více zapojuje do medicíny včetně gastroenterologie, což otevírá nové možnosti pro diagnostiku a léčbu onemocnění trávicího traktu. ChatGPT, AI model založený na architektuře GPT-4, má potenciál zrychlit diagnostiku a léčbu, personalizovat léčbu, vzdělávat a školit zdravotníky, podporovat rozhodování a zlepšovat komunikaci s pacienty. Avšak s využitím AI přicházejí i výzvy, jako omezená schopnost AI nahradit lidský úsudek, chyby v datech, otázky související s bezpečností a ochranou osobních údajů a náklady na implementaci. Budoucnost ChatGPT v gastroenterologii závisí na schopnosti zpracovávat a analyzovat velké množství dat pro identifikaci vzorů a tvorbu individuálních léčebných plánů. ChatGPT se díky pokroku v AI a strojovém učení stává přesnějším a efektivnějším, což umožní rychlejší diagnostiku a léčbu gastroenterologických onemocnění. V oblasti vzdělávání bude ChatGPT sloužit jako neocenitelný zdroj informací o nejnovějších výzkumných článcích a postupech. Přes výhody AI v gastroenterologii je důležité řešit otázky etiky, ochrany dat a spolupráce mezi AI a zdravotnickými odborníky. Zajištění správných protokolů a postupů umožní bezpečné a etické využití AI v medicíně. Ačkoli AI přináší významný potenciál pro zlepšení kvality péče, je třeba řešit výzvy spojené s ochranou dat, bezpečností a etikou.
Artificial intelligence (AI) is increasingly being incorporated into medicine, including gastroenterology, opening new possibilities for the diagnosis and treatment of digestive tract diseases. ChatGPT, an AI model based on the GPT-4 architecture, has the potential to accelerate diagnosis and treatment, personalize care, educate, and train healthcare professionals, support decision-making, and improve communication with patients. However, with the use of AI come challenges such as the limited ability of AI to replace human judgment, data errors, issues related to security and personal data protection, and implementation costs. The future of ChatGPT in gastroenterology depends on its ability to process and analyze large amounts of data to identify patterns and create individual treatment plans. Thanks to advancements in AI and machine learning, ChatGPT is becoming more accurate and efficient, enabling faster diagnosis and treatment of gastroenterological diseases. In the field of education, ChatGPT will serve as an invaluable source of information on the latest research articles and procedures. Despite the benefits of AI in gastroenterology, it is essential to address issues of ethics, data protection, and collaboration between AI and healthcare professionals. Ensuring proper protocols and procedures will enable the safe and ethical use of AI in medicine. Although AI offers significant potential for improving the quality of care, it is necessary to address challenges associated with data protection, security, and ethics.
- Keywords
- ChatGPT,
- MeSH
- Algorithms MeSH
- Data Analysis MeSH
- Gastroenterology * MeSH
- Humans MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Drug Discovery * MeSH
- Machine Learning MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
BACKGROUND: Artificial intelligence (AI) has advanced substantially in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fraudulent papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers. OBJECTIVE: The aim of this study was to investigate the capabilities of current AI language models in generating high-quality fraudulent medical articles. We hypothesized that modern AI models can create highly convincing fraudulent papers that can easily deceive readers and even experienced researchers. METHODS: This proof-of-concept study used ChatGPT (Chat Generative Pre-trained Transformer) powered by the GPT-3 (Generative Pre-trained Transformer 3) language model to generate a fraudulent scientific article related to neurosurgery. GPT-3 is a large language model developed by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts given by users. The model was trained on a massive corpus of text from the internet and is capable of generating high-quality text in a variety of languages and on various topics. The authors posed questions and prompts to the model and refined them iteratively as the model generated the responses. The goal was to create a completely fabricated article including the abstract, introduction, material and methods, discussion, references, charts, etc. Once the article was generated, it was reviewed for accuracy and coherence by experts in the fields of neurosurgery, psychiatry, and statistics and compared to existing similar articles. RESULTS: The study found that the AI language model can create a highly convincing fraudulent article that resembled a genuine scientific paper in terms of word usage, sentence structure, and overall composition. The AI-generated article included standard sections such as introduction, material and methods, results, and discussion, as well a data sheet. It consisted of 1992 words and 17 citations, and the whole process of article creation took approximately 1 hour without any special training of the human user. However, there were some concerns and specific mistakes identified in the generated article, specifically in the references. CONCLUSIONS: The study demonstrates the potential of current AI language models to generate completely fabricated scientific articles. Although the papers look sophisticated and seemingly flawless, expert readers may identify semantic inaccuracies and errors upon closer inspection. We highlight the need for increased vigilance and better detection methods to combat the potential misuse of AI in scientific research. At the same time, it is important to recognize the potential benefits of using AI language models in genuine scientific writing and research, such as manuscript preparation and language editing.
- MeSH
- Algorithms * MeSH
- Data Analysis MeSH
- Language MeSH
- Humans MeSH
- Semantics MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Tento přehledový článek se zaměřuje na základní principy technologií umělé inteligence (AI), možnosti jejich využití v medicíně a na příklady aplikací, které již byly začleněny do klinické praxe. Diskutuje také klíčové výzvy včetně etických otázek, jako je ochrana soukromí pacientů, algoritmická bias a problém transparentnosti modelů AI. Článek zdůrazňuje nutnost integrace AI do medicíny způsobem, který zajistí bezpečnost a důvěryhodnost, a současně vyzdvihuje význam vzdělávání zdravotnických profesionálů v oblasti AI. Umělá inteligence nabízí potenciál ke zlepšení přesnosti diagnostiky, efektivity péče a podpory při klinickém rozhodování, přičemž optimálních výsledků lze dosáhnout spoluprací mezi lékaři a systémy AI.
This review article focuses on the fundamental principles of artificial intelligence (AI) technologies, their utilisation in medicine, and examples of applications that have already been incorporated into clinical practice. It also discusses key challenges, including ethical issues such as patient data privacy, algorithmic bias, and the transparency problem of AI models. The article emphasizes the necessity of integrating AI into medicine in a manner that ensures safety and trustworthiness, while underscoring the importance of educating healthcare professionals about AI. Artificial intelligence offers the potential to enhance diagnostic accuracy, the efficiency of care, and support for clinical decision-making, with optimal outcomes being achieved through collaboration between physicians and AI systems.
- MeSH
- Algorithms MeSH
- Medicine * MeSH
- Humans MeSH
- Nephrology MeSH
- Artificial Intelligence * ethics MeSH
- Large Language Models MeSH
- Computer Security MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
