-
The Journal of Infection Oct 2023Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management... (Review)
Review
BACKGROUND
Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection.
OBJECTIVES
We summarise recent and potential future applications of AI and its relevance to clinical infection practice.
METHODS
1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions.
RESULTS
There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.
CONCLUSIONS
Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.
Topics: Humans; Artificial Intelligence; Deep Learning; COVID-19; Machine Learning; Algorithms
PubMed: 37468046
DOI: 10.1016/j.jinf.2023.07.006 -
Cold Spring Harbor Perspectives in... Feb 2024Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications... (Review)
Review
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications ranging from drug development and plastic degradation to carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion is relevant to machine learning-based design more broadly.
Topics: Machine Learning
PubMed: 38052497
DOI: 10.1101/cshperspect.a041469 -
Current Opinion in Gastroenterology Jul 2023The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial... (Review)
Review
PURPOSE OF REVIEW
The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients.
RECENT FINDINGS
Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment.
SUMMARY
Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.
Topics: Humans; Artificial Intelligence; Deep Learning; Inflammatory Bowel Diseases; Machine Learning; Precision Medicine
PubMed: 37144491
DOI: 10.1097/MOG.0000000000000945 -
Journal of Chemical Information and... Apr 2024
Topics: Cheminformatics; Machine Learning; Drug Discovery
PubMed: 38587006
DOI: 10.1021/acs.jcim.4c00444 -
American Journal of Epidemiology Nov 2023Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these... (Review)
Review
Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.
Topics: Humans; Deep Learning; Epidemiologists; Neural Networks, Computer; Algorithms; Machine Learning
PubMed: 37139570
DOI: 10.1093/aje/kwad107 -
Advanced Science (Weinheim,... Jan 2024At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that... (Review)
Review
At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.
Topics: Biocatalysis; Bioelectric Energy Sources; Biosensing Techniques; Machine Learning
PubMed: 37946709
DOI: 10.1002/advs.202306583 -
Frontiers in Endocrinology 2023
Topics: Artificial Intelligence; Machine Learning; Endocrinology
PubMed: 37522132
DOI: 10.3389/fendo.2023.1223931 -
Frontiers in Endocrinology 2023
Topics: Machine Learning; Artificial Intelligence
PubMed: 38164494
DOI: 10.3389/fendo.2023.1305897 -
Heart Rhythm Sep 2023
Topics: Defibrillators, Implantable; Heart; Algorithms; Machine Learning
PubMed: 37328130
DOI: 10.1016/j.hrthm.2023.06.008 -
Expert Opinion on Drug Discovery 2023As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep... (Review)
Review
INTRODUCTION
As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity.
AREAS COVERED
This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics.
EXPERT OPINION
Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
Topics: Humans; Artificial Intelligence; Deep Learning; Anti-Bacterial Agents; Neural Networks, Computer; Machine Learning
PubMed: 37794737
DOI: 10.1080/17460441.2023.2250721