Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department

A Dataset for Understanding Radiologist-Artificial Intelligence Collaboration

This dataset, Collab-CXR, provides a unique resource to study human-AI collaboration in chest X-ray interpretation. We present experimentally generated data from 227 professional radiologists who assessed 324 historical cases under varying information conditions: with and without AI assistance, and with and without clinical history. Using a custom-designed interface, we collected probabilistic assessments for 104 thoracic pathologies using a comprehensive hierarchical reporting structure. This dataset is the largest known comparison of human-AI collaborative performance to either AI or humans alone in radiology, offering assessments across an extensive range of pathologies with rich metadata on radiologist characteristics and decision-making processes. Multiple experimental designs enable both within-subject and between-subject analyses. Researchers can leverage this dataset to investigate how radiologists incorporate AI assistance, factors influencing collaborative effectiveness, and impacts on diagnostic accuracy, speed, and confidence across different cases and pathologies. By enabling rigorous study of human-AI integration in clinical workflows, this dataset can inform AI tool development, implementation strategies, and ultimately improve patient care through optimized collaboration in medical imaging.

A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings

Large foundation models show promise in biomedicine but face challenges in clinical use due to performance gaps, accessibility, cost, and lack of scalable evaluation. Here we show that open-source small multimodal models can bridge these gaps in radiology by generating free-text findings from chest X-ray images. Our data-centric approach leverages 697K curated radiology image-text pairs to train a specialized, domain-adapted chest X-ray encoder. We integrate this encoder with pre-trained language models via a lightweight adapter that aligns image and text modalities. To enable robust, clinically relevant evaluation, we develop and validate CheXprompt, a GPT-4-based metric for assessing factual accuracy aligned with radiologists' evaluations. Benchmarked with CheXprompt and other standard factuality metrics, LLaVA-Rad (7B) achieves state-of-the-art performance, outperforming much larger models like GPT-4V and Med-PaLM M (84B). While not immediately ready for real-time clinical deployment, LLaVA-Rad is a scalable, privacy-preserving and cost-effective step towards clinically adaptable multimodal AI for radiology.

Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications

Introduction

There is a growing adoption of artificial intelligence (AI) in the field of medical imaging. AI can potentially enhance patient care, improve workflow, and analyze patient's medical data. This study aimed to explore radiographers' knowledge, perceptions, and expectations toward integrating AI into medical imaging and to highlight one of the available applications of AI by evaluating an AI-based software that generates chest reports.

Methods

A cross-sectional survey was distributed to radiographers (n = 50) requesting information regarding demographics and knowledge of AI. In the retrospective part, chest radiographs were collected (n = 40), and an AI report was generated using Siemens AI software. A Likert scale was used by a radiologist to rate the report's accuracy. Ethical approval was obtained. Data are presented as mean ± SD.

Results

The survey results showed that most participants agreed that radiographers must adapt the AI technology, and they showed interest in taking courses about AI within radiography (98%, 92%, n = 50). Participants' opinions on AI correlated with their perceptions of AI education (p < 0.05, r = 0.307). The findings from the retrospective study showed that the radiologist agreed with 53% of the AI-generated chest reports.

Conclusion

The study findings identified a need for AI education and training for radiographers to increase their knowledge and improve their ability to use AI. Additionally, the study demonstrated that AI-powered tools are showing great promise in the field of medical imaging.

Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study

Background Multimodal generative artificial intelligence (AI) technologies can produce preliminary radiology reports, and validation with reader studies is crucial for understanding the clinical value of these technologies. Purpose To assess the clinical value of the use of a domain-specific multimodal generative AI tool for chest radiograph interpretation by means of a reader study. Materials and Methods A retrospective, sequential, multireader, multicase reader study was conducted using 758 chest radiographs from a publicly available dataset from 2009 to 2017. Five radiologists interpreted the chest radiographs in two sessions: without AI-generated reports and with AI-generated reports as preliminary reports. Reading times, reporting agreement (RADPEER), and quality scores (five-point scale) were evaluated by two experienced thoracic radiologists and compared between the first and second sessions from October to December 2023. Reading times, report agreement, and quality scores were analyzed using a generalized linear mixed model. Additionally, a subset of 258 chest radiographs was used to assess the factual correctness of the reports, and sensitivities and specificities were compared between the reports from the first and second sessions with use of the McNemar test. Results The introduction of AI-generated reports significantly reduced average reading times from 34.2 seconds ± 20.4 to 19.8 seconds ± 12.5 (P < .001). Report agreement scores shifted from a median of 5.0 (IQR, 4.0-5.0) without AI reports to 5.0 (IQR, 4.5-5.0) with AI reports (P < .001). Report quality scores changed from 4.5 (IQR, 4.0-5.0) without AI reports to 4.5 (IQR, 4.5-5.0) with AI reports (P < .001). From the subset analysis of factual correctness, the sensitivity for detecting various abnormalities increased significantly, including widened mediastinal silhouettes (84.3% to 90.8%; P < .001) and pleural lesions (77.7% to 87.4%; P < .001). While the overall diagnostic performance improved, variability among individual radiologists was noted. Conclusion The use of a domain-specific multimodal generative AI model increased the efficiency and quality of radiology report generation. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Babyn and Adams in this issue.

Large Language Models Applied to Health Care Tasks May Improve Clinical Efficiency, Value of Care Rendered, Research, and Medical Education

Large language models (LLMs) are generative artificial intelligence models that create content on the basis of the data on which it was trained. Processing capabilities have evolved from text only to being multimodal including text, images, audio, and video features. In health care settings, LLMs are being applied to several clinically important areas, including patient care and workflow efficiency, communications, hospital operations and data management, medical education, practice management, and health care research. Under the umbrella of patient care, several core use cases of LLMs include simplifying documentation tasks, enhancing patient communication (interactive language and written), conveying medical knowledge, and performing medical triage and diagnosis. However, LLMs warrant scrutiny when applied to health care tasks, as errors may have negative implications for health care outcomes, specifically in the context of perpetuating bias, ethical considerations, and cost-effectiveness. Customized LLMs developed for more narrow purposes may help overcome certain performance limitations, transparency challenges, and biases present in contemporary generalized LLMs by curating training data. Methods of customizing LLMs broadly fall under 4 categories: prompt engineering, retrieval augmented generation, fine-tuning, and agentic augmentation, with each approach conferring different information-retrieval properties for the LLM. LEVEL OF EVIDENCE: Level V, expert opinion.

Multimodal generative AI for medical image interpretation

Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as multimodal generative medical image interpretation (GenMI), create opportunities to automate parts of this complex process. In this Perspective, we synthesize progress and challenges in developing AI systems for generation of medical reports from images. We focus extensively on radiology as a domain with enormous reporting needs and research efforts. In addition to analysing the strengths and applications of new models for medical report generation, we advocate for a novel paradigm to deploy GenMI in a manner that empowers clinicians and their patients. Initial research suggests that GenMI could one day match human expert performance in generating reports across disciplines, such as radiology, pathology and dermatology. However, formidable obstacles remain in validating model accuracy, ensuring transparency and eliciting nuanced impressions. If carefully implemented, GenMI could meaningfully assist clinicians in improving quality of care, enhancing medical education, reducing workloads, expanding specialty access and providing real-time expertise. Overall, we highlight opportunities alongside key challenges for developing multimodal generative AI that complements human experts for reliable medical report writing.

GPT-4/4V's performance on the Japanese National Medical Licensing Examination

BACKGROUND

Recent advances in Artificial Intelligence (AI) are changing the medical world, and AI will likely replace many of the actions performed by medical professionals. The overall clinical ability of the AI has been evaluated by its ability to answer a text-based national medical examination. This study uniquely assesses the performance of Open AI's ChatGPT against all Japanese National Medical Licensing Examination (NMLE), including images, illustrations, and pictures.

METHODS

We obtained the questions of the past six years of the NMLE (112th to 117th) from the Japanese Ministry of Health, Labour and Welfare website. We converted them to JavaScript Object Notation (JSON) format. We created an application programming interface (API) to output correct answers using GPT-4 for questions without images and GPT4-V(ision) or GPT4 console for questions with images.

RESULTS

The percentage of image questions was 723/2400 (30.1%) over the past six years. In all years, GPT-4/4V exceeded the minimum score the examinee should score. In total, over the six years, the percentage of correct answers for basic medical knowledge questions was 665/905 (73.5%); for clinical knowledge questions, 1143/1531 (74.7%); and for image questions 497/723 (68.7%), respectively.

CONCLUSIONS

Regarding medical knowledge, GPT-4/4V met the minimum criteria regardless of whether the questions included images, illustrations, and pictures. Our study sheds light on the potential utility of AI in medical education.

Collaboration between clinicians and vision-language models in radiology report generation

Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial intelligence (AI)-generated reports. We build a state-of-the-art report generation system for chest radiographs, called Flamingo-CXR, and perform an expert evaluation of AI-generated reports by engaging a panel of board-certified radiologists. We observe a wide distribution of preferences across the panel and across clinical settings, with 56.1% of Flamingo-CXR intensive care reports evaluated to be preferable or equivalent to clinician reports, by half or more of the panel, rising to 77.7% for in/outpatient X-rays overall and to 94% for the subset of cases with no pertinent abnormal findings. Errors were observed in human-written reports and Flamingo-CXR reports, with 24.8% of in/outpatient cases containing clinically significant errors in both report types, 22.8% in Flamingo-CXR reports only and 14.0% in human reports only. For reports that contain errors we develop an assistive setting, a demonstration of clinician-AI collaboration for radiology report composition, indicating new possibilities for potential clinical utility.

Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward

CONTEXT.—

Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments.

OBJECTIVE.—

To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms.

DATA SOURCES.—

Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities.

CONCLUSIONS.—

GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and a population level.

Environment scan of generative AI infrastructure for clinical and translational science

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.

Precision emergency medicine

BACKGROUND

Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health.

METHODS

In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward.

RESULTS

Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty.

CONCLUSIONS

Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.

Black and white: how good are clinicians at diagnosing elbow injuries from paediatric elbow radiographs alone?

OBJECTIVES

Paediatric trauma elbow radiographs are difficult to interpret and there is a potential for harm if misdiagnosed. The primary goal of this study was to assess the ability of healthcare professionals internationally to interpret paediatric trauma elbow radiographs from the radiograph alone by formulating the correct diagnosis.

METHODS

This prospective international study was conducted online via the Free Open Access Medical Education platform, Don't Forget the Bubbles (DFTB, ISSN 2754-5407). Participants were recruited via the DFTB social media accounts between 17 August and 14 September 2021. Submissions that were incomplete or from participants who do not interpret paediatric elbow radiographs in their clinical practice were excluded. Participants completed an online survey of demographic data followed by interpreting 10 trauma-indicated elbow radiographs, by selecting multiple-choice options. The primary outcome was correct diagnosis.

RESULTS

Participant responses from 18 countries were analysed, with most responses from the UK, Australia and Ireland. Participants had backgrounds in emergency medicine (EM), paediatric emergency medicine (PEM), general practice (GP) and paediatrics, with over 70% having 6+ years of postgraduate experience. 3180 radiographs were interpreted by 318 healthcare professionals. Only nine (2.8%) participants correctly diagnosed all 10. The mean number of radiographs correctly interpreted was 5.44 (SD 2.3). The mean number for those with 6+ years of experience was 6.02 (SD 2.2). On reviewing the normal radiograph, 158 (49.7%) overcalled injuries. Participants with EM or PEM background were equally likely to have more correct answers than those from paediatric or GP backgrounds.

CONCLUSION

Globally, healthcare professional's success in correctly diagnosing paediatric elbow injuries from radiographs was suboptimal in this non-clinical exercise, despite capturing quite an experienced cohort of clinicians. This study has provided us with detailed baseline data to accurately assess the impact of interventions aimed at improving clinicians' interpretation of paediatric elbow radiographs in future studies.

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Prediction of presence and severity of metabolic syndrome using regional body volumes measured by a multisensor white-light 3D scanner and validation using a mobile technology

Aims

To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS).

Methods and results

We enrolled 1280 consecutive subjects who completed study protocol measurements, including 3D-BV and myBVI®. Body volumes and demographics were screened using the least absolute shrinkage and selection operator to select features associated with an MS severity score and prevalence. We randomly selected 80% of the subjects to train the models, and performance was assessed in 20% of the remaining observations and externally validated on 133 volunteers who prospectively underwent myBVI® measurements. The mean ± SD age was 43.7 ± 12.2 years, 63.7% were women, body mass index (BMI) was 28.2 ± 6.2 kg/m2, and 30.2% had MS and an MS severity z-score of -0.2 ± 0.9. Features β coefficients equal to zero were removed from the model, and 14 were included in the final model and used to calculate the body volume index (BVI), demonstrating an area under the receiving operating curve (AUC) of 0.83 in the validation set. The myBVI® cohort had a mean age of 33 ± 10.3 years, 61% of whom were women, 10.5% MS, an average MS severity z-score of -0.8, and an AUC of 0.88.

Conclusion

The described BVI model was associated with an increased severity and prevalence of MS compared with BMI and waist-to-hip ratio. Validation of the BVI had excellent performance when using myBVI®. This model could serve as a powerful screening tool for identifying MS.

Large language models and generative AI in telehealth: a responsible use lens

OBJECTIVE

This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI.

MATERIALS AND METHODS

Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions.

RESULTS

Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration.

CONCLUSION

The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases

BACKGROUND

As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results.

METHODS

This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response.

RESULTS

Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges.

CONCLUSIONS

While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation.

RELEVANCE TO CLINICAL PRACTICE

This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments.

PATIENT OR PUBLIC CONTRIBUTION

Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.

Applying AI to Safely and Effectively Scale Care to Address Chronic MSK Conditions

Background/Objectives: The rising prevalence of musculoskeletal (MSK) conditions has not been balanced by a sufficient increase in healthcare providers. Scalability challenges are being addressed through the use of artificial intelligence (AI) in some healthcare sectors, with this showing potential to also improve MSK care. Digital care programs (DCP) generate automatically collected data, thus making them ideal candidates for AI implementation into workflows, with the potential to unlock care scalability. In this study, we aimed to assess the impact of scaling care through AI in patient outcomes, engagement, satisfaction, and adverse events. Methods: Post hoc analysis of a prospective, pre-post cohort study assessing the impact on outcomes after a 2.3-fold increase in PT-to-patient ratio, supported by the implementation of a machine learning-based tool to assist physical therapists (PTs) in patient care management. The intervention group (IG) consisted of a DCP supported by an AI tool, while the comparison group (CG) consisted of the DCP alone. The primary outcome concerned the pain response rate (reaching a minimal clinically important change of 30%). Other outcomes included mental health, program engagement, satisfaction, and the adverse event rate. Results: Similar improvements in pain response were observed, regardless of the group (response rate: 64% vs. 63%; p = 0.399). Equivalent recoveries were also reported in mental health outcomes, specifically in anxiety (p = 0.928) and depression (p = 0.187). Higher completion rates were observed in the IG (79.9% (N = 19,252) vs. CG 70.1% (N = 8489); p < 0.001). Patient engagement remained consistent in both groups, as well as high satisfaction (IG: 8.76/10, SD 1.75 vs. CG: 8.60/10, SD 1.76; p = 0.021). Intervention-related adverse events were rare and even across groups (IG: 0.58% and CG 0.69%; p = 0.231). Conclusions: The study underscores the potential of scaling MSK care that is supported by AI without compromising patient outcomes, despite the increase in PT-to-patient ratios.

Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.

[Clinical Application of Artificial Intelligence-Based Detection Assistance Devices for Chest X-Ray Interpretation: Current Status and Practical Considerations]

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future

The utilization of artificial intelligence (AI) in medical imaging has become a rapidly growing field as a means to address contemporary demands and challenges of healthcare. Among the emerging applications of AI is point-of-care ultrasound (POCUS), in which the combination of these two technologies has garnered recent attention in research and clinical settings. In this Controversies paper, we will discuss the benefits, limitations, and future considerations of AI in POCUS for patients, clinicians, and healthcare systems.

Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry

The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.

Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications

Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated the world through the most recent wave of artificial intelligence chatbots, including ChatGPT. Transformers are a type of neural network architecture that enables better contextual understanding of language and efficient training on massive amounts of unlabeled data, such as unstructured text from the internet. As LLMs have increased in size, their improved performance and emergent abilities have revolutionized natural language processing. Since language is integral to human thought, applications based on LLMs have transformative potential in many industries. In fact, LLM-based chatbots have demonstrated human-level performance on many professional benchmarks, including in radiology. LLMs offer numerous clinical and research applications in radiology, several of which have been explored in the literature with encouraging results. Multimodal LLMs can simultaneously interpret text and images to generate reports, closely mimicking current diagnostic pathways in radiology. Thus, from requisition to report, LLMs have the opportunity to positively impact nearly every step of the radiology journey. Yet, these impressive models are not without limitations. This article reviews the limitations of LLMs and mitigation strategies, as well as potential uses of LLMs, including multimodal models. Also reviewed are existing LLM-based applications that can enhance efficiency in supervised settings.