Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as ‘abnormal’ or ‘normal’. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

Over the past 10 years, artificial intelligence (AI) has become one of the fastest-growing sectors in healthcare. There are now numerous new technologies designed to cut costs and improve diagnoses and treatment pathways. However, there is significant scepticism amongst National Health Service (NHS) staff regarding the usefulness of AI and it’s cost to the NHS. This has likely resulted in underuse and slow adoption of software that may revolutionize our healthcare system and ensure its continued survival and effectiveness. Several governing bodies have put forward guidance on the safe and effective adoption of AI tools, but this rarely covers the reality of selecting and deploying new software. This article set out clear guidance on the practicalities and pitfalls of deploying digital solutions in healthcare, using the example of a deep learning algorithm designed to improve the accuracy of chest X-ray (CXR) interpretation in the emergency department.

Background: The growing population of patients with adult congenital heart disease (ACHD) present complex lifelong care needs that traditional health systems are struggling to meet. Without innovation, gaps in access, timeliness and specialist oversight will widen. Digital health technologies, including artificial intelligence (AI), telemedicine, wearable devices and interoperable platforms offer a unique opportunity to transform care, but their potential in ACHD remain underexplored. Current developments: AI-driven prediction models show encouraging performance in mortality and event risk but require external validation and lesion-specific adaptation. Telemedicine, accelerated during the COVID-19 pandemic, has demonstrated safety and high patient satisfaction in selected cohorts, yet robust hybrid pathways are lacking. Wearables can capture rhythm, oxygen saturations and activity in real time, but consumer devices remain poorly validated for complex ACHD physiology. Data integration frameworks, such as federated learning, demonstrate global feasibility but face challenges in governance and interoperability. Future priorities: Progress in ACHD digital health will depend on three imperatives: (1) rigorous, prospective validation of digital tools in congenital populations; (2) equitable implementation, addressing digital literacy, infrastructure and reimbursement; (3) governance frameworks that embed specialist oversight, data privacy and cybersecurity from the outset. Conclusion: Digital health is no longer optional in ACHD care. The field risks falling behind broader cardiovascular innovation unless patients, clinicians, technologists and policymakers commit to specialist-led integration. A decisive shift towards validated, equitable and interoperable solutions can transform ACHD management into a more predictive, personalised and preventive discipline. The aim of this viewpoint is to highlight how digital technologies could strengthen ACHD care and define priorities for future adoption.