A review of top cardiology and cardiovascular medicine journal guidelines regarding the use of generative artificial intelligence tools in scientific writing

Understanding Surgeons' Reluctance to Adopt Intraoperative Coronary Graft Verification Procedures: A Literature Review Combined to AI-Driven Insights Under Human Supervision

Background: Intraoperative graft verification in coronary surgery is accepted worldwidand equally discussed. In spite of multiple sources of evidence published up to now in favor of clinical benefits following the use of the procedure, there is a persistent skepticism in adopting the available technologies. The object of the present review is to analyze the reluctance of surgeons toward the adoption of assessment methods. Materials and Method: A thorough literature review was carried out on Google Scholar based on the results obtained from AI's answer to the question about the reasons for that reluctance. We took advantage of using ChatGPT-4 since the research based on PubMed Central alone was not able to return a detailed response, maybe because the reasons for the reluctance are veiled in the text of the published papers. Through the items suggested by AI and taken from the literature, we deepened the research, pointing attention to the issues published so far about the various technologies. Results: There are many convincing pieces of evidence about the utility of intraoperative graft control in coronary surgery, involving improved clinical outcome, efficacy and safety, and social cost saving. The opinion that arose through this analysis is that, beyond the objective difficulties in utilizing some technologies and the equally objective limitations of an economic and organizational nature, the reluctance is the result of a real unwillingness based on the various implications that the discovery of the technical error entails. Conclusions: This negative attitude, in light of the convincing scientific and clinical evidence published up to now, appears to overwhelm the benefits for patients.

Do Ophthalmology Journals Have AI Policies for Manuscript Writing?

PURPOSE

To assess the prevalence of artificial intelligence (AI) usage policies in manuscript writing in PubMed-indexed ophthalmology journals and examine the relationship between the adoption of these policies and journal characteristics.

DESIGN

Cross-sectional study.

SUBJECTS

PubMed-indexed ophthalmology journals.

MAIN OUTCOME MEASURES

Prevalence of policies in journal guidelines regarding the use of AI in manuscript writing.

METHODS

We reviewed the guidelines of 84 ophthalmology journals indexed in PubMed to determine the presence of AI-use policies for manuscript generation. We further compared journal metrics, such as CiteScore, Journal Impact Factor (JIF), Journal Citation Indicator (JCI), Source Normalized Impact per Paper (SNIP), and SCImago Journal Rank (SJR), between journals with and without AI policies. Additionally, we analyzed the association between AI policy adoption and journal characteristics, such as MEDLINE indexing and society affiliation.

RESULTS

Among the 84 journals, 53 (63.1%) had AI policies for manuscript generation, with no significant changes observed during the study period. Journals indexed in MEDLINE were significantly more likely to have AI policies (68.8%) than non-MEDLINE-indexed journals, where no AI policies were found (0%) (P = .0008). There was no significant difference in AI policy adoption between society-affiliated (62.7%) and unaffiliated journals (64.7%) (P = .84). Journals with AI policies had significantly higher metrics, including CiteScore, SNIP, SJR, JIF, and JCI (P < .05).

CONCLUSIONS

While many ophthalmology journals have adopted AI policies, the lack of guidelines in over one-third of journals highlights a critical need for consistent and comprehensive AI policies, particularly as the AI landscape rapidly advances.

Health Data Sciences and Cardiovascular Disease in Africa: Needs and the Way Forward

PURPOSE OF REVIEW

The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes.

RECENT FINDINGS

While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region.

Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions

Cardiovascular diseases remain the leading cause of morbidity and mortality globally, posing significant challenges to public health. The rapid evolution of artificial intelligence (AI), particularly with large language models such as ChatGPT, has introduced transformative possibilities in cardiovascular medicine. This review examines ChatGPT's broad applications in enhancing clinical decision-making-covering symptom analysis, risk assessment, and differential diagnosis; advancing medical education for both healthcare professionals and patients; and supporting research and academic communication. Key challenges associated with ChatGPT, including potential inaccuracies, ethical considerations, data privacy concerns, and inherent biases, are discussed. Future directions emphasize improving training data quality, developing specialized models, refining AI technology, and establishing regulatory frameworks to enhance ChatGPT's clinical utility and mitigate associated risks. As cardiovascular medicine embraces AI, ChatGPT stands out as a powerful tool with substantial potential to improve therapeutic outcomes, elevate care quality, and advance research innovation. Fully understanding and harnessing this potential is essential for the future of cardiovascular health.

Revolutionizing Cardiology With Words: Unveiling the Impact of Large Language Models in Medical Science Writing

Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including-but not limited to-generating titles, identifying key words, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer-review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts, and lack originality. These limitations underscore the importance of human oversight in using LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine.

Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest

Artificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality and the nature of authorship, we conducted a competitive essay contest in 2024 with both human and AI participants. Human authors and 4 distinct LLMs generated essays on controversial topics in stroke care and outcomes research. A panel of Stroke Editorial Board members (mostly vascular neurologists), blinded to author identity and with varying levels of AI expertise, rated the essays for quality, persuasiveness, best in topic, and author type. Among 34 submissions (22 human and 12 LLM) scored by 38 reviewers, human and AI essays received mostly similar ratings, though AI essays were rated higher for composition quality. Author type was accurately identified only 50% of the time, with prior LLM experience associated with improved accuracy. In multivariable analyses adjusted for author attributes and essay quality, only persuasiveness was independently associated with odds of a reviewer assigning AI as author type (adjusted odds ratio, 1.53 [95% CI, 1.09-2.16]; P=0.01). In conclusion, a group of experienced editorial board members struggled to distinguish human versus AI authorship, with a bias against best in topic for essays judged to be AI generated. Scientific journals may benefit from educating reviewers on the types and uses of AI in scientific writing and developing thoughtful policies on the appropriate use of AI in authoring manuscripts.

Automated machine learning model for fundus image classification by health-care professionals with no coding experience

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CDFL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.