In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create. In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create. SEE EDITORIALS, P. XX. 

The AI Future of Emergency Medicine.

Als Nebenprodukt einer starken Zunahme von hochauflösender radiologischer (Schnitt‑)Bildgebung steigt die Prävalenz von Zufallsbefunden („incidental findings“, IF) seit Jahren an. Die Entdeckung eines Zufallsbefundes kann die frühzeitige Behandlung einer möglicherweise schwerwiegenden Erkrankung ermöglichen und den Krankheitsverlauf so entscheidend verändern. Viele Zufallsbefunde haben allerdings keine oder nur geringe gesundheitliche Auswirkungen, und dennoch führt ihre Entdeckung häufig zu einer Kaskade zusätzlicher Untersuchungen und Interventionen. Es ist unstrittig, dass Zufallsbefunde einen direkten Einfluss auf das Leben der betroffenen Personen haben können und nicht nur psychosoziale Aspekte wie bspw. Sorgen und Ängste durch falsch-positive Befunde eine Rolle spielen, sondern unter Umständen auch versicherungsrechtliche oder berufliche Probleme auftreten können. Deshalb rufen der richtige Umgang mit Zufallsbefunden und die dabei geltenden ethischen Herausforderungen regelmäßig Diskussionen hervor. Allgemeine Prinzipien, die bei der Behandlung von Zufallsbefunden zu berücksichtigen sind, umfassen einerseits die Verantwortung für das Wohlergehen des Patienten/Studienteilnehmers selbst und andererseits auch die Verantwortung für Wohlergehen der möglicherweise mitbetroffenen Gesellschaft. Um Überdiagnosen und Übertherapien zu vermeiden und einen hohen Nutzen für die Patienten zu erzielen, müssen Radiologen und Kliniker wissen, wie man mit Zufallsbefunden umgeht. In den letzten Jahren wurden von verschiedenen nationalen und internationalen Gesellschaften wichtige Leitlinien („White Papers“) zum Management von IF veröffentlicht. Es ist wichtig, dass Radiologen diese Leitlinien genau kennen und einhalten und dann auch für überweisende Ärzte zur Diskussion und Beratung zur Verfügung stehen. Bei allen Entscheidungen muss dabei immer der Patient im Mittelpunkt stehen. 

Patientenzentriertes, wertbasiertes Management von radiologischen Zufallsbefunden

BACKGROUND
Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field.


OBJECTIVE
This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care.


METHODS
We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest.


RESULTS
This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella.


CONCLUSIONS
As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence's impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.

Unveiling the Influence of Artificial Intelligence on Advancements in Respiratory Care: A Narrative Review (Preprint)

Abstract The growing demand for imaging services in the U.S., driven by an aging population and the rise in chronic diseases, has contributed to a significant radiology workforce shortage. Simultaneously, supply-side constraints, including limited radiology residency positions and substantial retirements, have exacerbated this gap. These imbalances increase patient wait times, risk diagnostic delays, and contribute to radiologist burnout. Artificial intelligence (AI) offers potential solutions by addressing three primary areas: demand management, workflow efficiency, and capacity building. First, to manage demand, AI tools can leverage predictive analytics and decision-support systems to reduce unnecessary imaging and prioritize high-value imaging examinations. Next, AI can streamline tasks and boost efficiency with applications such as automated scheduling, assisted report generation, and image quality checks. Finally, by enhancing education, facilitating remote collaboration, improving patient communication, and offering advanced image interpretation assistance, AI can expand radiologists’ capabilities, improve retention, and enhance long-term workforce sustainability. By integrating these approaches, radiology can address workforce shortages while upholding the highest standards of patient care. 

AI solutions to the radiology workforce shortage

Abstract Background Patients with COPD are often affected by loss of bone mineral density (BMD) and osteoporotic fractures. Natriuretic peptides (NP) are known as cardiac markers, but have also been linked to fragility-associated fractures in the elderly. As their functions include regulation of fluid and mineral balance, they also might affect bone metabolism, particularly in systemic disorders such as COPD. Research question We investigated the association between NP serum levels, vertebral fractures and BMD assessed by chest computed tomography (CT) in patients with COPD. Methods Participants of the COSYCONET cohort with CT scans were included. Mean vertebral bone density on CT (BMD-CT) as a risk factor for osteoporosis was assessed at the level of TH12 (AI-Rad Companion), and vertebral compression fractures were visually quantified by two readers. Their relationship with N-terminal pro-B-type natriuretic peptide (NT-proBNP), Mid-regional pro-atrial natriuretic peptide (MRproANP) and Midregional pro-adrenomedullin (MRproADM) was determined using group comparisons and multivariable analyses. Results Among 418 participants (58% male, median age 64 years, FEV 1 59.6% predicted), vertebral fractures in TH12 were found in 76 patients (18.1%). Compared to patients without fractures, these had elevated serum levels ( p ≤ 0.005) of MRproANP and MRproADM. Using optimal cut-off values in multiple logistic regression analyses, MRproANP levels ≥ 65 nmol/l (OR 2.34; p = 0.011) and age ( p = 0.009) were the only significant predictors of fractures after adjustment for sex, BMI, smoking status, FEV 1 % predicted, SGRQ Activity score, daily physical activity, oral corticosteroids, the diagnosis of cardiac disease, and renal impairment. Correspondingly, MRproANP ( p &lt; 0.001), age ( p = 0.055), SGRQ Activity score ( p = 0.061) and active smoking ( p = 0.025) were associated with TH12 vertebral density. Interpretation MRproANP was a marker for osteoporotic vertebral fractures in our COPD patients from the COSYCONET cohort. Its association with reduced vertebral BMD on CT and its known modulating effects on fluid and ion balance are suggestive of direct effects on bone mineralization. Trial registration ClinicalTrials.gov NCT01245933, Date of registration: 18 November 2010. 

Midregional Proatrial Natriuretic Peptide (MRproANP) is associated with vertebral fractures and low bone density in patients with chronic obstructive pulmonary disease (COPD)

https://www.mdpi.com/2227-9032/10/11/2166/pdf?version=1667039914

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

Air trapping (AT) is a frequent finding in early cystic fibrosis (CF) lung disease detectable by imaging. The correct radiographic assessment of AT on paired inspiratory-expiratory computed tomography (CT) scans is laborious and prone to inter-reader variation. Conventional threshold-based methods for AT quantification are primarily designed for adults and less suitable for children. The administered radiation dose, in particular, plays an important role, especially for children. Low dose (LD) CT is considered established standard in pediatric lung CT imaging but also ultra-low dose (ULD) CT is technically feasible and requires comprehensive validation. We investigated a deep learning approach to quantify air trapping on ULDCT in comparison to LDCT and assessed structure-function relationships by cross-validation against multiple breath washout (MBW) lung function testing. A densely connected convolutional neural network (DenseNet) was trained on 2-D patches to segment AT. The mean threshold from radiographic assessments, performed by two trained radiologists, was used as ground truth. A grid search was conducted to find the best parameter configuration. Quantitative AT (QAT), defined as the percentage of AT in the lungs detected by our DenseNet models, correlated strongly between LD and ULD. Structure-function relationships were maintained. The best model achieved a patch-based DICE coefficient of 0.82 evaluated on the test set. AT percentages correlated strongly with MBW results (LD: $$R = 0.76$$ , $$p < 0.001$$ ; ULD: $$R = 0.78$$ , $$p < 0.001$$ ). A strong correlation between LD and ULD ( $$R = 0.96$$ , $$p < 0.001$$ ) and small ULD-LD differences (mean difference $$-1.04 \pm 3.25\%$$ ) were observed. 

Deep Learning-Based Air Trapping Quantification Using Paired Inspiratory-Expiratory Ultra-low Dose CT

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AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

Deep Learning-Based Air Trapping Quantification Using Paired Inspiratory-Expiratory Ultra-low Dose CT