Editorial on Special Issue “Artificial Intelligence in Pathological Image Analysis”

Accurate patient outcome prediction in the intensive care unit (ICU) can potentially lead to more effective and efficient patient care. Deep learning models are capable of learning from data to accurately predict patient outcomes, but they typically require large amounts of data and computational resources. Transfer learning (TL) can help in scenarios where data and computational resources are scarce by leveraging pretrained models. While TL has been widely used in medical imaging and natural language processing, it has been rare in electronic health record (EHR) analysis. Furthermore, domain adaptation (DA) has been the most common TL method in general, whereas inductive transfer learning (ITL) has been rare. To the best of our knowledge, DA and ITL have never been studied in-depth in the context of EHR-based ICU patient outcome prediction. This study investigated DA, as well as rarely researched ITL, in EHR-based ICU patient outcome prediction under simulated, varying levels of data scarcity. Two patient cohorts were used in this study: (1) eCritical, a multicenter ICU data from 55,689 unique admission records from 48,672 unique patients admitted to 15 medical-surgical ICUs in Alberta, Canada, between March 2013 and December 2019, and (2) Medical Information Mart for Intensive Care III, a single-center, publicly available ICU data set from Boston, Massachusetts, acquired between 2001 and 2012 containing 61,532 admission records from 46,476 patients. We compared DA and ITL models with baseline models (without TL) of fully connected neural networks, logistic regression, and lasso regression in the prediction of 30-day mortality, acute kidney injury, ICU length of stay, and hospital length of stay. Random subsets of training data, ranging from 1% to 75%, as well as the full data set, were used to compare the performances of DA and ITL with the baseline models at various levels of data scarcity. Overall, the ITL models outperformed the baseline models in 55 of 56 comparisons (all P values <.001). The DA models outperformed the baseline models in 45 of 56 comparisons (all P values <.001). ITL resulted in better performance than DA in terms of the number of times and the margin with which it outperformed the baseline models. In 11 of 16 cases (8 of 8 for ITL and 3 of 8 for DA), TL models outperformed baseline models when trained using 1% data subset. TL-based ICU patient outcome prediction models are useful in data-scarce scenarios. The results of this study can be used to estimate ICU outcome prediction performance at different levels of data scarcity, with and without TL. The publicly available pretrained models from this study can serve as building blocks in further research for the development and validation of models in other ICU cohorts and outcomes.

Medical Visual Question Answering (MedVQA) is a dual research field that intersects medical imaging and natural language processing, for better interpretability and accessibility of medical image data.Medical image quality is paramount for accurate diagnostics and subsequent medical visual question answering (MedVQA) tasks. This research focuses on applying and then analyzing results of different denoising methods on VQA-RAD, Medical VQA dataset to enhance quality of images. This study explores effectiveness of different traditional and deep learning based methods to reduce noise within medical images, thereby improving the accuracy and reliability of MedVQA task. We applied different traditional denoising filtering methods such as, gaussian filter, median filter, average filter, bilateral filter and convolutional autoencoder (CAE) based on deep learning on a VQA-RAD dataset to compare effectiveness of each denoising methods to improve image quality. Through comprehensive experiments and evaluations, this paper demonstrates that the convolutional autoencoder is potentially enhancing quality of medical images with an emphasis on preserving essential diagnostic information while suppressing unwanted noise with compare to other traditional denoising filters. The denoised images are then employed as input to improve accuracy for MedVQA tasks. The results of this research will help in optimizing medical imaging pipelines, ultimately benefiting clinical decision-making and healthcare outcomes.

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.

Temporal reasoning with medical data—A review with emphasis on medical natural language processing

Deep learning has emerged as a powerful tool in various domains, revolutionising machine learning research. However, one persistent challenge is the scarcity of labelled training data, which hampers the performance and generalisation of deep learning models. To address this limitation, researchers have developed innovative methods to overcome data scarcity and enhance deep model learning capabilities. Two prevalent techniques that have gained significant attention are transfer learning and self-supervised learning. Transfer learning leverages knowledge learned from pre-training on a large-scale dataset, such as ImageNet, and applies it to a target task with limited labelled data. This approach allows models to benefit from the learned representations and effectively transfer knowledge to new tasks, resulting in improved learning performance and generalisation. On the other hand, self-supervised learning focuses on training models using pretext tasks that do not require manual annotation, allowing them to learn valuable representations from large amounts of unlabelled data. These learned representations can then be fine-tuned for downstream tasks, mitigating the need for extensive labelled data. In recent years, transfer and self-supervised learning have found applications in various fields, including medical image processing, video recognition, and natural language processing. These approaches have demonstrated remarkable achievements, enabling breakthroughs in areas such as disease diagnosis, object recognition, and language understanding. However, while these methods offer numerous advantages, they also have limitations. For example, transfer learning may face domain mismatch issues between the pre-training and target domains, while self-supervised learning requires careful design of pretext tasks to ensure meaningful representations. This review paper explores the recent applications of these pre-training methods in various fields within the past three years. It delves into the advantages and limitations of each approach, assesses the performance of models employing these techniques, and identifies potential directions for future research. By providing a comprehensive review of current pre-training methods, this article offers guidance for selecting the best technique for specific deep learning applications to address the data scarcity issue.

Explainable deep learning diagnostic system for prediction of lung disease from medical images

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

The pervasive deployment of Deep Learning models has recently prompted apprehensions regarding their ecological footprint, owing to the exorbitant levels of energy consumption necessitated by the training and inference processes. The term "Red AI" is employed to denote artificial intelligence (AI) models that undergo training using resource-intensive methodologies on very large datasets. This practice can engender substantial energy usage and emissions of carbon, thereby opposing "Green AI." The latter concept alludes to AI models designed for similar efficiency and reduced environmental impact. This objective is realized through the utilization of smaller datasets, less computationally intensive training techniques, or sustainable energy resources. While Red AI prioritizes accuracy and performance, Green AI emphasizes efficiency and sustainability. Given that both paradigms exhibit advantages and limitations, the debates around the topics have burgeoned in the scientific arena, delving into novel algorithms, hardware innovations, and improved data utilization techniques aimed at mitigating the ecological consequences of intricate applications such as GPT and BERT. Nevertheless, due to the relative novelty of this debate, not much effort has been dedicated yet to contextualizing the essence of Red AI and the prospects of Green AI in a coherent framework. Within this context, the present work contributes by meticulously delineating both domains through a multifaceted analysis of their causes and ramifications, described from the points of computer architectures, data structures, and algorithms. Additionally, the study reviews notable instances of study cases based on complex Red AI models. The primary contribution of this article encompasses a comprehensive survey of Red and Green AI, stemming from a selection of the literature performed by the authors, subsequently organized into distinct clusters. These clusters encompass i) articles that qualitatively or quantitatively address the issue of Red AI, identifying Green AI as a plausible remedy, ii) articles offering insights into the environmental impact associated with the deployment of extensive Deep Learning models, and iii) articles introducing the techniques underpinning Green AI, aiming at mitigating the cost of Red AI. The outcome emerging from the analysis performed by this work consists of a compromise between sustainability in contrast to the performance of AI tools. Unless the complex training and inference procedures of software models mitigate their environmental impact, it will be necessary to decrease the level of accuracy of production systems, inevitably conflicting with the objective of the major AI vendors. The outcomes of this work would be beneficial to scholars pursuing intricate Deep Learning architectures in scientific research, as well as AI enterprises struggling with the protracted training demands of commercial products within the realms of Computer Vision and Natural Language Processing.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

HomeRadiology: Artificial IntelligenceVol. 1, No. 1 Next EditorialFree AccessArtificial Intelligence, Real RadiologyCharles E. Kahn, Jr Charles E. Kahn, Jr Charles E. Kahn, Jr Published Online:Jan 30 2019https://doi.org/10.1148/ryai.2019184001MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In IntroductionGlendower:I can call spirits from the vasty deep.Hotspur:Why, so can I, or so can any man; But will they come when you do call for them?– William Shakespeare, Henry IV, Part IWelcome to this inaugural issue of Radiology: Artificial Intelligence. Our journal’s mission is to publish high-quality scientific work that advances our understanding of artificial intelligence (AI) in radiology.AI has become a topic of great interest—especially the application of machine learning techniques to medical images—but AI itself is not new. The term artificial intelligence was proposed in 1956 to describe efforts to understand, simulate, and improve upon human qualities such as reasoning, learning, solving problems, understanding verbal and written language, processing visual information, and playing games like chess and poker.What is new is a resurgence of interest in AI, particularly in the use of machine learning to recognize patterns in images. And, curiously, it is game playing that has opened this new frontier—but not the games of chess, checkers, or Go. Rather, think Xbox and PlayStation. Video games require a rapidly changing three-dimensional scene to be transformed into two-dimensional images shown in real time. The need to compute images efficiently spurred the development of highly parallelized graphics processing units. These specialized processors, in turn, have powered software for increasingly complex and sophisticated “deep” artificial neural network models. Whereas neural networks developed 10 years ago typically had three or four layers, today’s deep networks comprise hundreds of layers (1).Deep learning models have engendered both great excitement and a great deal of hyperbole. After all, if AI systems can pick out pictures of cats on the web, then surely such systems are ready to replace radiologists, right? Well, perhaps not, at least not right now. There is much work to be done to build and validate systems that can detect and characterize the thousands of imaging findings and their associated diseases that can be seen across a panoply of radiology studies.And that brings us to the quote from Shakespeare. Anyone can claim to build an AI system, but that doesn’t mean that the system will do their bidding as imagined. Our journal is here to assure that the science and applications of AI in radiology are built on thoughtful, innovative, and well-validated research.What sorts of topics will this journal publish? We will bring you the same high caliber of research that is found in RSNA’s flagship scientific journal, Radiology, but focused here on AI, machine learning, and data science in radiology. In particular, we seek to publish first-rate work that provides rigorous evaluation of AI’s applications to clinical problems in radiology.We invite manuscripts that show the impact of AI to extract information, diagnose and manage disease in patients, streamline radiology workflow, or improve health care outcomes. We’re interested in image segmentation, image reconstruction, automated detection of abnormalities, diagnostic reasoning, natural language processing, clinical workflow analysis, radiomics, and radiogenomics. We also invite manuscripts that demonstrate novel applications of AI in radiology or highlight innovative AI methodologies. Developers of publicly available sets of radiologic images, image annotations, radiology reports, or algorithms can present their work as a Data Resources report.AI and radiology do not exist in isolation: they are part of broad endeavors to advance knowledge and improve health. As such, this journal will feature articles on the ethical, legal, social, and economic implications of AI in radiology.AI is and must be a human—and humane—activity (2). We must engage in this work with an eye to how these technologies will help us care for our patients more effectively and humanely. Our goal is not to replace, but rather to extend our human abilities to provide medical care—and to improve the lives of those we are privileged to serve.All RSNA members receive access to this online bimonthly journal. We invite all readers (RSNA members or not) to sign up for our Editor’s Blog, The Vasty Deep (https://pubs.rsna.org/page/ai/blog) and to follow us on Twitter (@Radiology_AI). These social media platforms will augment the journal and offer innovative online features.Again, welcome!

Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare.A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging.Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals.Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.

Summary Objectives : Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field. Methods : We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019. Results : Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation. Conclusions : The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.

The Natural Language Processing (NLP) a computational used for reducing the distance between human and the machine. It means NLP facilitate human to communicate with the machine easily. NLP is a subfield of linguistics and artificial intelligence. NLP can be defined as the automatic manipulation of natural language like speech and text by using python models. The conversation between humans and machines has never been much easier and it is bound to be better in the upcoming decade. The paper distinguishes four phases by discussing a brief introduction to NLP and components of NLP followed by current applications of NLP, effect of NLP models on small businesses and future scope of it in the field of healthcare. This paper also deals with the future trends and applications of NLP in the sectors such as Sales and Marketing, Healthcare, Digital Humanities, IT and Security among others. Deep learning models and classifiers both will be needed for smooth functioning of future applications.KeywordsNatural Language ProcessingMachine learningLanguage analysisLanguage modelingLanguage processingSpeech recognition

Chapter 11 - Artificial intelligence for pathology