scholarly journals Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption

2020 ◽  
Vol 36 (6) ◽  
pp. 443-449
Author(s):  
Julian Varghese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Research ◽  
Structured Data ◽  
Regulatory Aspects ◽  
Clinical Benefits ◽  
The Subject ◽  
Common Barriers ◽  
Clinical Adoption

<b><i>Background:</i></b> Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. <b><i>Summary:</i></b> Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. <b><i>Key Messages:</i></b> There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.

Recent Progress in Quantum Machine Learning

2021 ◽  
pp. 232-256
Author(s):  
Amandeep Singh Bhatia ◽  
Renata Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantum Computing ◽  
Recent Progress ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Exciting Field ◽  
Quantum Machine Learning ◽  
The Subject ◽  
Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

scholarly journals The challenge of clinical adoption—the insurmountable obstacle that will stop machine learning?

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20180017
Author(s):  
Jonathan Taylor ◽  
John Fenner
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Health Economics ◽  
Clinical Environment ◽  
New Approach ◽  
Representative Data ◽  
Potential Benefits ◽  
Insurmountable Obstacle ◽  
Learning Software ◽  
Clinical Adoption

Machine learning promises much in the field of radiology, both in terms of software that can directly analyse patient data and algorithms that can automatically perform other processes in the reporting pipeline. However, clinical practice remains largely untouched by such technology. This article highlights what we consider to be the major obstacles to widespread clinical adoption of machine learning software, namely: representative data and evidence, regulations, health economics, heterogeneity of the clinical environment and support and promotion. We argue that these issues are currently so substantial that machine learning will struggle to find acceptance beyond the narrow group of applications where the potential benefits are readily evident. In order that machine learning can fulfil its potential in radiology, a radical new approach is needed, where significant resources are directed at reducing impediments to translation rather than always being focused solely on development of the technology itself.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Artificial intelligence in human genomics and biomedicine

2021 ◽  
Vol 30 (3) ◽  
pp. 30-36
Author(s):  
Reinhard Heil ◽  
Nils B. Heyen ◽  
Martina Baumann ◽  
Bärbel Hüsing ◽  
Daniel Bachlechner ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Individual Differences ◽  
Biomedical Research ◽  
Innovation System ◽  
Complex Data ◽  
Causal Knowledge ◽  
Human Genomics ◽  
Key Issues

The increasing availability of extensive and complex data has made human genomics and its applications in (bio)medicine an at­ tractive domain for artificial intelligence (AI) in the form of advanced machine learning (ML) methods. These methods are linked not only to the hope of improving diagnosis and drug development. Rather, they may also advance key issues in biomedicine, e. g. understanding how individual differences in the human genome may cause specific traits or diseases. We analyze the increasing convergence of AI and genom­ics, the emergence of a corresponding innovation system, and how these associative AI methods relate to the need for causal knowledge in biomedical research and development (R&D) and in medical prac­tice. Finally, we look at the opportunities and challenges for clinical practice and the implications for governance issues arising from this convergence.

Simulation of neural network for determination of technical state of internal state engine (ICE)

2020 ◽  
pp. 74-78
Author(s):  
K. Sovin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Internal State ◽  
Optimal Solution ◽  
Technical State ◽  
Mathematical Functions ◽  
The Subject ◽  
Human Thinking

The article considers Artificial Intelligence (AI), which is science and technology, where the idea of modeling the processes of human thinking by using the capabilities of the computer is laid down. Machine learning, which can be imagined as a set of certain algorithms and methods that allow computers to be trained to obtain specific conclusions for the subject matter on the basis of available data. Neural networks are controlled by complex mathematical functions and solve problems of increased complexity using mathematical models built on analytical or other methods. Efficient neural network is reduced to creation of optimal solution as a result of analysis and conversion of incoming parameters of network for determination of technical state of ICE engine.

scholarly journals Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

2021 ◽  
Author(s):  
Riemer H. J. A. Slart ◽  
Michelle C. Williams ◽  
Luis Eduardo Juarez-Orozco ◽  
Christoph Rischpler ◽  
Marc R. Dweck ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computed Tomography ◽  
Clinical Practice ◽  
Nuclear Cardiology ◽  
European Association ◽  
Imaging Techniques ◽  
Cardiovascular Imaging ◽  
Position Paper ◽  
Daily Clinical Practice

AbstractIn daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Brain Computations

2020 ◽  
Author(s):  
Edmund T. Rolls
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Brain Function ◽  
Computational Science ◽  
Theoretical Physics ◽  
Medical Sciences ◽  
Computational Approaches ◽  
Potential Applications ◽  
The Subject ◽  
The Brain

The subject of this book is how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed. The book will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.

scholarly journals Key challenges for delivering clinical impact with artificial intelligence

BMC Medicine ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Christopher J. Kelly ◽  
Alan Karthikesalingam ◽  
Mustafa Suleyman ◽  
Greg Corrado ◽  
Dominic King
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Evaluation ◽  
Performance Metrics ◽  
Main Body ◽  
Negative Consequences ◽  
Technical Accuracy ◽  
Potential Applications ◽  
Randomised Controlled

Abstract Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Main body Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. Conclusion The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.

scholarly journals Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Albert K. Feeny ◽  
Mina K. Chung ◽  
Anant Madabhushi ◽  
Zachi I. Attia ◽  
Maja Cikes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Patient Outcomes ◽  
Cardiac Electrophysiology ◽  
Recent Literature ◽  
Human Capabilities ◽  
Key Points ◽  
Detection And Diagnosis

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

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