The integration of artificial intelligence (AI) in cardiovascular imaging has revolutionized the field, offering significant advancements in diagnostic accuracy and clinical efficiency. However, the complexity and opacity of AI models, particularly those involving machine learning (ML) and deep learning (DL), raise critical legal and ethical concerns due to their "black box" nature. This manuscript addresses these concerns by providing a comprehensive review of AI technologies in cardiovascular imaging, focusing on the challenges and implications of the black box phenomenon. We begin by outlining the foundational concepts of AI, including ML and DL, and their applications in cardiovascular imaging. The manuscript delves into the "black box" issue, highlighting the difficulty in understanding and explaining AI decision-making processes. This lack of transparency poses significant challenges for clinical acceptance and ethical deployment. The discussion then extends to the legal and ethical implications of AI's opacity. The need for explicable AI systems is underscored, with an emphasis on the ethical principles of beneficence and non-maleficence. The manuscript explores potential solutions such as explainable AI (XAI) techniques, which aim to provide insights into AI decision-making without sacrificing performance. Moreover, the impact of AI explainability on clinical decision-making and patient outcomes is examined. The manuscript argues for the development of hybrid models that combine interpretability with the advanced capabilities of black box systems. It also advocates for enhanced education and training programs for healthcare professionals to equip them with the necessary skills to utilize AI effectively. Patient involvement and informed consent are identified as critical components for the ethical deployment of AI in healthcare. Strategies for improving patient understanding and engagement with AI technologies are discussed, emphasizing the importance of transparent communication and education. Finally, the manuscript calls for the establishment of standardized regulatory frameworks and policies to address the unique challenges posed by AI in healthcare. By fostering interdisciplinary collaboration and continuous monitoring, the medical community can ensure the responsible integration of AI into cardiovascular imaging, ultimately enhancing patient care and clinical outcomes.

Objectives: Artificial intelligence (AI) promises to accelerate and democratize medical imaging, yet low- and middle-income countries (LMICs) face distinct barriers to adoption. This perspective identifies those barriers and proposes an action-oriented roadmap. Materials and methods: Insights were synthesized from a Johns Hopkins Science Diplomacy Hub workshop (18 experts in radiology, AI, and health policy) and a scoping review of peer-reviewed and grey literature. Workshop discussions were transcribed, thematically coded, and iteratively validated to reach consensus. Results: Five interlocking barriers were prioritized: (1) infrastructure gaps—scarce imaging devices, unstable power, and limited bandwidth; (2) data deficiencies—small, non-representative, or ethically constrained datasets; (3) workforce shortages and brain drain; (4) uncertain ethical, regulatory, and medicolegal frameworks; and (5) financing and sustainability constraints. Case studies from Nigeria, Uganda, and Colombia showed that low-field MRI, cloud-based PACS, community-engaged data collection, and public–private partnerships can successfully mitigate several of these challenges. Conclusions: Targeted policy levers—including shared procurement of low-cost hardware, regional AI and data hubs, train-the-trainer workforce programs, and harmonized regulation—can enable LMIC health systems to deploy AI imaging responsibly, shorten diagnostic delays, and improve patient outcomes. Lessons are transferable to resource-constrained settings worldwide. Key Points: Question How can LMICs overcome infrastructure, data, workforce, regulatory, and financing barriers to implement artificial-intelligence tools in clinical medical imaging ? Findings Our multinational consensus identifies five obstacles and maps each to actionable levers: low-cost hardware, regional data hubs, train-the-trainer schemes, harmonized regulation, blended financing . Clinical relevance Implementing these targeted measures enables LMIC health systems to deploy AI imaging reliably, shorten diagnostic delays, and improve patient outcomes while reducing dependence on external expertise .

Retatrutide is a novel triple agonist targeting the receptors of glucagon-like peptide 1 (GLP-1), gastric inhibitory polypeptide (GIP), and glucagon. We sought to assess the efficacy and safety of retatrutide in obese patients with or without diabetes. PubMed, Scopus, Web of Science, and Cochrane databases were searched from inception until May 2024. Eligible studies comprised randomized controlled trials that compared retatrutide with placebo in obese patients. We excluded studies on healthy populations, non-English texts, single-arm studies, animal studies, and abstracts. RevMan software (version 5.4) was used for analysis, with subgroup evaluation by dose (4 mg, 8 mg, 12 mg). Three randomized controlled trials, encompassing 878 patients, satisfied our inclusion criteria. Retatrutide significantly reduced body weight (mean difference [MD]: −14.33%), body mass index (MD: −5.38), waist circumference (MD: −10.51 cm), fasting plasma glucose (MD: −23.51 mg/dL), hemoglobin A1c (MD: −0.91%), and systolic and diastolic blood pressure (MD: −9.88 mm Hg and −3.88 mm Hg, respectively), all with P values < 0.00001. No significant difference in adverse events was observed between the groups (relative risk: 1.11, P = 0.24). Retatrutide demonstrated significant improvements in body weight and metabolic outcomes among adults with obesity and had an appropriate safety profile. However, additional large and long-term trials are required to establish these results.

Background: The integration of artificial intelligence (AI) chatbots in medicine is expanding rapidly, with notable models like ChatGPT by OpenAI, Gemini by Google, and Copilot by Microsoft. These chatbots are increasingly used to provide medical information, yet their reliability in specific areas such as cardiovascular imaging remains underexplored. This study aims to evaluate the accuracy and reliability of ChatGPT (versions 3.5 and 4), Gemini, and Copilot in responding to patient inquiries about cardiovascular imaging. Methods: We sourced 30 patient-oriented questions on cardiovascular imaging. The questions were submitted to ChatGPT-4, ChatGPT-3.5, Copilot Balanced Mode, Copilot Precise Mode, and Gemini. Responses were evaluated by two cardiovascular radiologists based on accuracy, clarity, completeness, neutrality, and appropriateness using a structured rubric. Inter-rater reliability was assessed using Cohen’s Kappa. Results: ChatGPT-4 achieved the highest performance with 78.3% accuracy, 86.87% clarity and appropriateness, 81.7% completeness, and 100% neutrality. Gemini showed balanced performance, while Copilot Balanced Mode excelled in clarity and accuracy but lagged in completeness. Copilot Precise Mode had the lowest scores in completeness and accuracy. Penalty assessments revealed that ChatGPT-4 had the lowest incidence of missing or misleading information. Conclusion: ChatGPT-4 emerged as the most reliable AI model for providing accurate, clear, and comprehensive patient information on cardiovascular imaging. While other models showed potential, they require further refinement. This study underscores the value of integrating AI chatbots into clinical practice to enhance patient education and engagement.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI’s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging.