Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.

Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges

There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understanding how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, to avoid unintended biases related to the "black box" nature of complex models. In this commentary, we review and discuss some relevant topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges, with particular attention paid to interpretability and explainability of employed models. As in other fields of medicine, the exponential growth of artificial intelligence and ML indicates the potential for improving the efficacy of antimicrobial stewardship interventions, at least in part by reducing time-consuming tasks for overwhelmed health care personnel. Improving our knowledge about how complex ML models work could help to achieve crucial advances in promoting the appropriate use of antimicrobials, as well as in preventing antimicrobial resistance selection and dissemination.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Specific and Nonspecific Effects of Influenza Vaccines

With the introduction of the influenza vaccine in the official immunization schedule of most countries, several data regarding the efficacy, tolerability, and safety of influenza immunization were collected worldwide. Interestingly, together with the confirmation that influenza vaccines are effective in reducing the incidence of influenza virus infection and the incidence and severity of influenza disease, epidemiological data have indicated that influenza immunization could be useful for controlling antimicrobial resistance (AMR) development. Knowledge of the reliability of these findings seems essential for precise quantification of the clinical relevance of influenza immunization. If definitively confirmed, these findings can have a relevant impact on influenza vaccine development and use. Moreover, they can be used to convince even the most recalcitrant health authorities of the need to extend influenza immunization to the entire population. In this narrative review, present knowledge regarding these particular aspects of influenza immunization is discussed. Literature analysis showed that the specific effects of influenza immunization are great enough per se to recommend systematic annual immunization of younger children, old people, and all individuals with severe chronic underlying diseases. Moreover, influenza immunization can significantly contribute to limiting the emergence of antimicrobial resistance. The problem of the possible nonspecific effects of influenza vaccines remains unsolved. The definition of their role as inducers of trained immunity seems essential not only to evaluate how much they play a role in the prevention of infectious diseases but also to evaluate whether they can be used to prevent and treat clinical conditions in which chronic inflammation and autoimmunity play a fundamental pathogenetic role.

Cryosphere: a frozen home of microbes and a potential source for drug discovery

The world is concerned about the emergence of pathogens and the occurrence and spread of antibiotic resistance among pathogens. Drug development requires time to combat these issues. Consequently, drug development from natural sources is unavoidable. Cryosphere represents a gigantic source of microbes that could be the bioprospecting source of natural products with unique scaffolds as molecules or drug templates. This review focuses on the novel source of drug discovery and cryospheric environments as a potential source for microbial metabolites having potential medicinal applications. Furthermore, the problems encountered in discovering metabolites from cold-adapted microbes and their resolutions are discussed. By adopting modern practical approaches, the discovery of bioactive compounds might fulfill the demand for new drug development.

Use of Biomaterials in 3D Printing as a Solution to Microbial Infections in Arthroplasty and Osseous Reconstruction

The incidence of microbial infections in orthopedic prosthetic surgeries is a perennial problem that increases morbidity and mortality, representing one of the major complications of such medical interventions. The emergence of novel technologies, especially 3D printing, represents a promising avenue of development for reducing the risk of such eventualities. There are already a host of biomaterials, suitable for 3D printing, that are being tested for antimicrobial properties when they are coated with bioactive compounds, such as antibiotics, or combined with hydrogels with antimicrobial and antioxidant properties, such as chitosan and metal nanoparticles, among others. The materials discussed in the context of this paper comprise beta-tricalcium phosphate (β-TCP), biphasic calcium phosphate (BCP), hydroxyapatite, lithium disilicate glass, polyetheretherketone (PEEK), poly(propylene fumarate) (PPF), poly(trimethylene carbonate) (PTMC), and zirconia. While the recent research results are promising, further development is required to address the increasing antibiotic resistance exhibited by several common pathogens, the potential for fungal infections, and the potential toxicity of some metal nanoparticles. Other solutions, like the incorporation of phytochemicals, should also be explored. Incorporating artificial intelligence (AI) in the development of certain orthopedic implants and the potential use of AI against bacterial infections might represent viable solutions to these problems. Finally, there are some legal considerations associated with the use of biomaterials and the widespread use of 3D printing, which must be taken into account.

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

BACKGROUND

The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.

METHOD

This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.

RESULTS

Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.

CONCLUSION

AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

A nested cohort 5-year Canadian surveillance of Gram-negative antimicrobial resistance for optimized antimicrobial therapy

We analyzed 5 years (2016-2020) of nested Canadian data from the Study for Monitoring Antimicrobial Resistance Trends (SMART) to identify pathogen predominance and antimicrobial resistance (AMR) patterns of adult Gram-negative infections in Canadian health care and to complement other public surveillance programs and studies in Canada. A total of 6853 isolates were analyzed from medical (44%), surgical (18%), intensive care (22%) and emergency units (15%) and from respiratory tract (36%), intra-abdominal (25%), urinary tract (24%) and bloodstream (15%) infections. Overall, E. coli (36%), P. aeruginosa (18%) and K. pneumoniae (12%) were the most frequent isolates and P. aeruginosa was the most common respiratory pathogen. 18% of Enterobacterales species were ESBL positive. Collective susceptibility profiles showed that P. aeruginosa isolates were highly susceptible (> 95%) to ceftolozane/tazobactam and colistin, though markedly less susceptible (58-74%) to other antimicrobials tested. Multi-drug resistance (MDR) was present in 10% of P. aeruginosa isolates and was more frequent in those from respiratory infections and from ICU than non-ICU locations. Of P. aeruginosa isolates that were resistant to combinations of ceftazidime, piperacillin/tazobactam and meropenem, 73-96% were susceptible to ceftolozane/tazobactam over the period of the study. These national data can now be combined with clinical prediction rules and genomic data to enable expert antimicrobial stewardship applications and guide treatment policies to optimize adult patient care.

Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review

Introduction: The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI) has been explored to improve antibiotic (AB) prescribing, and thereby control and reduce ABR. This review explores whether the use of AI can improve antibiotic prescribing for human patients. Methods: Observational studies that use AI to improve antibiotic prescribing were retrieved for this review. There were no restrictions on the time, setting or language. References of the included studies were checked for additional eligible studies. Two independent authors screened the studies for inclusion and assessed the risk of bias of the included studies using the National Institute of Health (NIH) Quality Assessment Tool for observational cohort studies. Results: Out of 3692 records, fifteen studies were eligible for full-text screening. Five studies were included in this review, and a narrative synthesis was carried out to assess their findings. All of the studies used supervised machine learning (ML) models as a subfield of AI, such as logistic regression, random forest, gradient boosting decision trees, support vector machines and K-nearest neighbours. Each study showed a positive contribution of ML in improving antibiotic prescribing, either by reducing antibiotic prescriptions or predicting inappropriate prescriptions. However, none of the studies reported the engagement of AB prescribers in developing their ML models, nor their feedback on the user-friendliness and reliability of the models in different healthcare settings. Conclusion: The use of ML methods may improve antibiotic prescribing in both primary and secondary settings. None of the studies evaluated the implementation process of their models in clinical practices. Prospero Registration: (CRD42022329049).

Short-term antibiotic therapy for the most common bacterial respiratory infections in infants and children

Overuse and misuse of antibiotics have strongly accelerated the progressive increase in bacterial antimicrobial resistance (AMR). The evidence that antimicrobial selective pressure was greater the longer the antibiotic therapy was continued has led some experts to reconsider duration of antibiotic therapy testing the use of short-term drug administration. If as effective as long-term therapy, short-term therapy could have been an easy measure to limit AMR emergence. In the present narrative review, whether present knowledge on short-term therapy of acute streptococcal pharyngitis (ASF), acute otitis media (AOM) and mild to moderate community-acquired pneumonia (CAP) allows systematic use of short-term therapy in infants and children with these diseases is discussed. Literature analysis showed that reducing the duration of antibiotic therapy for some of the most common pediatric respiratory infections could be a valid measure to contain the antibiotic abuse and the consequent impact on the emergence of AMR. Several data seem to indicate that this type of intervention is possible, as short-term therapy has been found as effective as the traditionally recommended long-term therapy in some cases of ASF, AOM and mild to moderate CAP. However, further studies are needed to better characterize infants and children who can have benefit with short-term antibiotic therapy in common bacterial respiratory infections.

Artificial Intelligence in Biological Sciences

Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.

Prevention of Surgical Site Infections in Neonates and Children: Non-Pharmacological Measures of Prevention

A surgical site infection (SSI) is an infection that occurs in the incision created by an invasive surgical procedure. Although most infections are treatable with antibiotics, SSIs remain a significant cause of morbidity and mortality after surgery and have a significant economic impact on health systems. Preventive measures are essential to decrease the incidence of SSIs and antibiotic abuse, but data in the literature regarding risk factors for SSIs in the pediatric age group are scarce, and current guidelines for the prevention of the risk of developing SSIs are mainly focused on the adult population. This document describes the current knowledge on risk factors for SSIs in neonates and children undergoing surgery and has the purpose of providing guidance to health care professionals for the prevention of SSIs in this population. Our aim is to consider the possible non-pharmacological measures that can be adopted to prevent SSIs. To our knowledge, this is the first study to provide recommendations based on a careful review of the available scientific evidence for the non-pharmacological prevention of SSIs in neonates and children. The specific scenarios developed are intended to guide the healthcare professional in practice to ensure standardized management of the neonatal and pediatric patients, decrease the incidence of SSIs and reduce antibiotic abuse.

Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates

Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.

Antimicrobial Compounds from Microorganisms

Antimicrobial resistance is an exigent public health concern owing to the emergence of novel strains of human resistant pathogens and the concurrent rise in multi-drug resistance. An influx of new antimicrobials is urgently required to improve the treatment outcomes of infectious diseases and save lives. Plant metabolites and bioactive compounds from chemical synthesis have found their efficacy to be dwindling, despite some of them being developed as drugs and used to treat human infections for several decades. Microorganisms are considered untapped reservoirs for promising biomolecules with varying structural and functional antimicrobial activity. The advent of cost-effective and convenient model organisms, state-of-the-art molecular biology, omics technology, and machine learning has enhanced the bioprospecting of novel antimicrobial drugs and the identification of new drug targets. This review summarizes antimicrobial compounds isolated from microorganisms and reports on the modern tools and strategies for exploiting promising antimicrobial drug candidates. The investigation identified a plethora of novel compounds from microbial sources with excellent antimicrobial activity against disease-causing human pathogens. Researchers could maximize the use of novel model systems and advanced biomolecular and computational tools in exploiting lead antimicrobials, consequently ameliorating antimicrobial resistance.

The remaining unsolved problems for rational antibiotic therapy use in pediatric community-acquired pneumonia

INTRODUCTION

Despite availability of several official guidelines, not all the problems related to the most effective and safe use of antibiotics in children with community-acquired pneumonia (CAP) have been solved. Presently, too many children receive unneeded antibiotics or, when antibiotics are mandatory, the choice of the drug is not appropriate.

AREAS COVERED

In this paper, the authors discuss the remaining unsolved problems for rational antibiotic therapy use in pediatric community-acquired pneumonia and provide their expert perspectives.

EXPERT OPINION

Further improvement in pediatric CAP management could be derived from physician education on antibiotic use and a larger use, particularly in office practice, of point of care testing or new technologies (i.e. artificial intelligence) to define etiology of a lower respiratory infection. However, recommendations regarding the duration of antibiotic therapy vary largely because of the absence of reliable data on the optimal CAP treatment according to the bacterial etiology of the disease, its severity, and child characteristics. Available evidence seems to confirm that a short course of antibiotics, approximately 5 days, can be effective and lead to results not substantially different from those obtained with prolonged-course antibiotic therapy, at least in patients with mild to moderate disease.

An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction

Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.

Retrospective 8-Year Study on the Antibiotic Resistance of Uropathogens in Children Hospitalised for Urinary Tract Infection in the Emilia-Romagna Region, Italy

The development and spread of antibiotic resistance is an increasingly important global public health problem, even in paediatric urinary tract infection (UTI). In light of the variability in the data, it is necessary to conduct surveillance studies to determine the prevalence of antibiotic resistance in specific geographical areas to optimize therapeutic management. In this observational, retrospective, multicentre study, the medical records of 1801 paediatric patients who were hospitalised for UTI between 1 January 2012, and 30 June 2020, in Emilia-Romagna, Italy, were analysed. Escherichia coli was the most frequently detected pathogen (75.6%), followed by Klebsiella pneumoniae (6.9%) and Pseudomonas aeruginosa (2.5%). Overall, 840 cases (46.7%) were due to antimicrobial-resistant uropathogens: 83 (4.7%) extended spectrum beta-lactamase (ESBL)-producing, 119 (6.7%) multidrug resistant (MDR) and 4 (0.2%) extensively drug resistant (XDR) bacteria. Empirical antibiotic therapy failed in 172 cases (9.6%). Having ESBL or MDR/XDR uropathogens, a history of recurrent UTI, antibiotic therapy in the preceding 30 days, and empirical treatment with amoxicillin or amoxicillin/clavulanate were significantly associated with treatment failure, whereas first-line therapy with third-generation cephalosporins was associated with protection against negative outcomes. In conclusion, the increase in the resistance of uropathogens to commonly used antibiotics requires continuous monitoring, and recommendations for antibiotic choice need updating. In our epidemiological context, amoxicillin/clavulanate no longer seems to be the appropriate first-line therapy for children hospitalised for UTI, whereas third-generation cephalosporins continue to be useful. To further limit the emergence of resistance, every effort to reduce and rationalise antibiotic consumption must be implemented.

Gut Microbiota and Obesity in Adults and Children: The State of the Art

In recent decades, obesity has become a serious public health problem affecting both children and adults. Considering the multifactorial origin of obesity, including modifiable factors, childhood was identified as the golden age for investing in obesity prevention by both promoting proper lifestyles and actively intervening in possible triggers. The gut microbiota is at the center of the most recent scientific studies and plays a key role in obesity development because it is intimately linked to energetic-humoral variations in the host: its alterations can promote a state of excessive energy storage, and it can be manipulated to maintain energy homoeostasis. This review aims to offer a panoramic understanding of the interplay between obesity and the gut microbiota, focusing on the contribution that the gut microbiota could have to the prevention of childhood obesity and its complications in adulthood. Currently, the use of some specific probiotic strains has been shown to be able to act on some secondary metabolic consequences of obesity (such as liver steatosis and insulin resistance) without any effect on weight loss. Although definitive conclusions cannot be drawn on the real impact of probiotics and prebiotics, there is no doubt that they represent an exciting new frontier in the treatment of obesity and associated metabolic dysfunctions. Targeted studies randomized on specific populations and homogeneous for ethnicity, sex, and age are urgently needed to reach definitive conclusions about the influence of microbiota on weight. In particular, we still need more studies in the pediatric population to better understand when the switch to an obese-like gut microbiota takes place and to better comprehend the right timing of each intervention, including the use of pre/probiotics, to improve it.

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

BACKGROUND

The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals.

OBJECTIVE

This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany.

METHODS

A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals.

RESULTS

The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research.

CONCLUSIONS

Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

Prevention of New Respiratory Episodes in Children with Recurrent Respiratory Infections: An Expert Consensus Statement

In healthy infants and young children, the development of respiratory tract infections (RTIs) is extremely common. In this paper, we present an international consensus of the available approaches for the prevention of recurrent RTIs in children, including the atopic/allergic ones as well as those with asthma. Few convincing measures for reducing the frequency and clinical relevance of recurrent respiratory episodes in RTI-prone children have been developed until now. Among the most recently suggested measures, immunotherapy is attractive, but only for OM-85 is there a sufficient number of well-conducted clinical trials confirming efficacy in RTIs prevention with an adequate safety profile. In the case of probiotics, it is not clear which bacteria can offer the best results and which dosage and schedule of administration are the most effective. The problems of dosage and the schedule of administration are not solved also for vitamin D, despite some promising efficacy results. While we wait for new knowledge, the elimination or reduction as much as possible of the environmental factors that favor RTIs, vaccination when available and/or indicated, and the systematic application of the traditional methods for infection prevention, such as hand washing, remain the best measures to prevent recurrent infections in RTI-prone children.