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Background and aim:
Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze large amounts of data (big data) in real-time, providing forecasts that can support the clinician's...
Context in source publication
Context 1
... do Nascimento et al., analyzing the impact of big data analysis on health indicators and core priorities described in the World Health Organization (WHO) General Program of Work 2019Work /2023 and in the European Program of Work (EPW). The article highlighted how the accuracy and management of some chronic diseases can be improved by supporting real-time analysis for diagnostic and predictive purposes (5) (Figure 1). ...
Citations
... DL techniques can perform more accurate image analysis and segmentation and are more commonly used in diagnosing and classifying tumours. 2,4,6 Radiomics has been used to predict tumour grades, and now, in the form of radio-genomics, DL allows correlation between genetic markers and their imaging phenotypes. In neuro-oncology, this is being used to predict prognostic molecular markers such as isocitrate dehydrogenase (IDH) mutation, methylguanine-DNA methyltransferase methylation, and 1p/19q co-deletion with accuracies of 83-94%. ...
Image learning involves using artificial intelligence (AI) to analyse radiological images. Various machine and deeplearning- based techniques have been employed to process images and extract relevant features. These can later be used to detect tumours early and predict their survival based on their grading and classification. Radiomics is now also used to predict genetic mutations and differentiate between tumour progression and treatment-related side effects. These were once completely dependent on invasive procedures like biopsy and histopathology. The use and feasibility of these techniques are now widely being explored in neurooncology to devise more accurate management plans and limit morbidity and mortality. Continue...
... ML is a subset of AI that enables systems to learn from data, identify patterns and make decisions with minimal human intervention. It involves algorithms that improve the performance in a given task through increased exposure to data [17]. In the context of healthcare, particularly polypharmacy, ML can process vast amounts of patient data-clinical histories, genetic information and real-time health metrics-to identify risks, suggest personalised medication plans and predict potential adverse drug reactions [17,18]. ...
... It involves algorithms that improve the performance in a given task through increased exposure to data [17]. In the context of healthcare, particularly polypharmacy, ML can process vast amounts of patient data-clinical histories, genetic information and real-time health metrics-to identify risks, suggest personalised medication plans and predict potential adverse drug reactions [17,18]. ...
... AI has the capability to analyse patient data, such as their histories, laboratory results and medication profiles, to provide personalised treatment recommendations and avoid the potential negative effects of polypharmacy. By considering individual patient characteristics and potential drug interactions, AI systems can assist health professionals in optimising medication regimens and reducing the risks associated with polypharmacy [17,18]. For example, one systematic review examined 63 studies that utilised AI methods in precision cancer medicine. ...
Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.
... ( Bellini et al, 2022 ) . ...
از دوره آنتروپوسن تاکنون، به دلیل گستردگی فعالیت‌های انسان و بهره‌برداری بیش از ظرفیت از منابع طبیعی، تعداد و شدت تاثیر مخاطرات محیطی نیز افزایش یافته است. در بیشتر مواقع، وقوع مخاطرات با خسارات مالی و جانی همراه می‌باشد. به نحویکه بخشي از اين خسارت، به دلیل عدم آگاهي از واقعه و بخش ديگر، به واسطه عدم آگاهي در مقابله با واقعه است. انقلاب صنعتی سوم (عصر دیجیتال) و چهارم (عصر هوش مصنوعی)، به جهت استفاده گسترده از داده و تجزیه و تحلیل فضایی آنها، امکان مدیریت مناسبتری را جهت کاهش اثرات مخاطرات محیطی فراهم نموده است. این پژوهش که مبنتی بر ساختار اکتشافی- کاربردی است، به موضوع اهمیت و جایگاه هوش مصنوعی در جهان و کاربرد آن در مدیریت مخاطرات محیطی و محیط زیست پرداخته است. از سال 2014 مقالات علمی متعددی در زمینه کاربرد هوش مصنوعی به چاپ رسیده است. اما از سال 2017 تاکنون محققان به دنبال درک شهودی هوش مصنوعی در نوع کاربرد خود بودند. به طوریکه استفاده از این علم با کاربرد در منابع طبیعی و محیط زیست همچون پهپادها برای جلوگیری از آتش سوزی جنگل‌ها، در بررسی تغییرات آب و هوایی، توسعه سیستم‌های هشدار اولیه تخریب سرزمین و بیابان‌زایی، فرونشست زمین، مانیتورینگ حیات وحش، امنیت غذایی، گرد و غبار و فرسایش خاک روند رو به رشدی را نشان می‌دهد. کاربرد و توسعه هوش مصنوعی در مطالعات منابع طبیعی و محیط زیست، امکان مدیریت ریسک (احتمال وقوع خطر) به جای مدیریت بحران (وقوع خطر) و کاهش اثر مخاطرات محیطی را فراهم می‌نماید.
... Let's think about the introduction of tools such as SpO2 or continuous invasive blood pressure monitoring into clinical practice: the application of AI could be even more impactful. Anesthesia and postoperative intensive care can provide the necessary fuel for the application of new technologies thanks to the production of large amounts of data, thus becoming fertile areas for their development [1]. ...
The development of artificial intelligence (AI) is disruptive and unstoppable, also in medicine. Because of the enormous quantity of data recorded during continuous monitoring and the peculiarity of our specialty where stratification and mitigation risk are some of the core aspects, anesthesiology and postoperative intensive care are fertile fields where new technologies find ample room for expansion.
Recently, research efforts have focused on the development of a holistic technology that globally embraces the entire perioperative period rather than a fragmented approach where AI is developed to carry out specific tasks. This could potentially revolutionize the perioperative medicine we know today. In fact, AI will be able to expand clinician's ability to interpret, adapt, and ultimately act in a complex reality with facets that are too complex to be managed all at the same time and in a holistic manner.
With the support of new tools, as healthcare professionals we have the moral obligation to govern this transition, allowing an ethical and sustainable development of these technologies and avoiding being overwhelmed by them. We should welcome this transhumanist tension which does not aim at the replacement of human capabilities or even at the integration of these but rather at the expansion of a “single intelligence”.
... Conducting predictive studies using artificial intelligence (AI) and machine learning (ML) would be an intriguing prospect [37]. In this regard, the use of digital technology is recommended by the EAPAC [2]. ...
Background
Despite being a useful strategy for providing respiratory support to patients with advanced or terminal illnesses, non-invasive ventilation (NIV) requires in-depth investigation in several key aspects.
Objectives
This bibliometric analysis seeks to comprehensively examine the existing research on the subject. Its goal is to uncover valuable insights that can inform the prediction trajectory of studies, guide the implementation of corrective measures, and contribute to the improvement of research networks.
Methods
A comprehensive review of literature on NIV in the context of palliative care was conducted using the Web of Science core collection online database. The search utilized the key terms “non-invasive ventilation” and “palliative care” to identify the most relevant articles. All data were gathered on November 7, 2023. Relevant information from documents meeting the specified criteria was extracted, and Journal Citation Reports™ 2022 (Clarivate Analytics) served as the data source. The analysis employed literature analysis and knowledge visualization tools, specifically CiteScope (version 6.2.R4) and VOSviewer (version 1.6.20).
Results
A dataset with bibliometric findings from 192 items was analyzed. We found a consistent upward of the scientific output trend over time. Guidelines on amyotrophic lateral sclerosis management received the highest number of citations. Most documents were published in top-ranked journals. Less than one-third of the documents pertain to clinical studies, especially retrospective analyses (25%). Key topics such as “decision making”, and “communication” were less addressed.
Conclusions
Given the substantial clinical implications, further high-quality studies on this subject are recommended. Encouraging international collaborations is needed. Despite the growing volume of documents in the field, this bibliometric analysis indicates a decline in collaborative networks.
... The study emphasized on how to promote real-time analysis for diagnosis and predictive purposes for enhancing the accuracy and management of various diseases. [6] History of the studies published on artificial intelligence in dental application was given by Bellini et al. [ Table 1]. [6] Neural network is the ML technique inspired in biological neurons where the input is fed to one or multiple layers to produce an output. ...
... [6] History of the studies published on artificial intelligence in dental application was given by Bellini et al. [ Table 1]. [6] Neural network is the ML technique inspired in biological neurons where the input is fed to one or multiple layers to produce an output. There are various types of neural networks like; Deep neural network, which is a type of neural network with multiple hidden layers, making more complex feature construction data, convolutional neural network (CNN), which is a special type of neural network. ...
In dentistry, artificial intelligence (AI) has shown great potential in improving diagnosis, treatment planning, and patient care. AI algorithms have been developed to analyze dental images, such as radiographs and intraoral scans, aiding in the detection of dental caries, periodontal diseases, and oral lesions. These algorithms can provide more accurate and efficient diagnoses, reducing the reliance on human interpretation. AI has also been utilized in treatment planning and helping dentists to determine the optimal approach for procedures such as dental implant placement and orthodontic treatment, and recently, AI has also been playing a significant role in forensic medicine as well as in forensic odontology. In addition, AI-based chatbots and virtual assistants have been developed to provide patients with personalized oral health information and guidance. Despite the numerous advantages, challenges remain in implementing AI in dentistry, such as ensuring data privacy and addressing ethical concerns. Nevertheless, AI has the potential to revolutionize dentistry by improving diagnosis, treatment planning, and patient care, ultimately leading to better oral health outcomes. This review provides an overview of the current applications of AI and its influence on dental practice, along with future prospects.
... [ Figure 2] illustrates the pros and cons of AI in research and analysis, leaving the question open to debate whether open AI is a boon or a bane to the scientific community. [6,[42][43][44] Moore et al. 3Fs limiting the confidence in research [45] As a useful guide to comprehending the features which can limit confidence in the contemporary literature, Moore et al., alongside narrating the multitude of relevant factors, present a context-appropriate classification of the same into the 3Fs, eventually culminating as either (F)lawed, (F)utile, or (F) abricated research [ Table 2]. [45] FUTURE DIRECTIONS e quintessential altercation in the subject regarding the peak of the pyramid continues to intensify with some arguing in favor of well-conducted RCTs mimicking realworld situations better than any other study design, thus minimizing the likelihood of confounding. ...
... [46] We believe the "3Es" would be instrumental in achieving this pinnacle, i.e., adequately backed by robust (E)vidence, physician (E)xperience, and meeting the patient (E)xpectations. [6,[42][43][44] [45] • Flawed (prone to imperfections/errors) Due to bias in the study, low standard of study design and execution, risk of carrying forward flaws into SRs and MAs • Futile (unnecessary, irrelevant, and adds no value) ...
Evidence-based medicine (EBM) undeniably classifies as a pre-eminent advance in the clinical approach to decision-making. Although EBM as a topic has been discussed at length, it is more about the process of integrating EBM into practice, wherein the actual debate becomes even more interesting with unique roadblocks cropping up at the very end of the translational highway. Meanwhile, the core concept of EBM has stood firm over decades; it is likely the research landscape and the corresponding intricacies continue to evolve at a rather rampant pace. Evidence-based practice is thus best elaborated in close conjunction with the recent advent of precision medicine, the impact of the coronavirus disease 2019 pandemic, and the ever-compounding present-age research concerns.
In this reference, the randomized controlled trials and now the meta-analysis (second-order analysis of analyses) are also being increasingly scrutinized for the contextual veracities and how the quality of the former can be rendered more robust to strengthen our epic pyramid of EBM. Withstanding, the index narrative article is a
modern-day take on EBM keeping abreast of the evolving opportunities and challenges, with the noble objective of deliberating a standpoint that aims to potentially bridge some of the existing gaps in the translation of research to patient care and outcome improvement, at large.
Keywords: Coronavirus disease 2019, Evidence-based medicine, Meta-analysis, Precision medicine, Randomized
controlled trials, Systematic reviews, Research
... The proliferation of AI drove this requirement. Due to technical advancements, AI in modern medicine is a burgeoning domain in detection, grading, genome analysis, diagnostic imaging, including image analysis, decision making, and prognosis prediction, characterizing diseases, and control measures [101,102]. In the field of AI, the phrases deep learning (DL) and ML are of paramount significance. ...
The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID’s challenges and impact on public health.
... On the other hand, DL employs intricate algorithms that are designed to mimic the structure of the human brain, enabling the processing of unstructured data such as text, images, and documents. DL is a distinct subfield of ML, which in turn is a subcategory of AI [102,103]. ...
The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.
... Te advent of DL marked a signifcant turning point in the feld of AI, fundamentally changing the way AI systems are developed and applied. Processes of CV, FL, and NLP involve the development of ANNs which, in their complexity, are part of DL [11]. ...
Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.
