Generative AI: a new dawn in cardiovascular study and research

Dear Editor,

The introduction of generative artificial intelligence (AI) across various domains has stirred significant excitement and debate. Especially in cardiovascular research, tools exemplified by OpenAI’s ChatGPT (Generative Pre-trained Transformer) and similar models offer a tantalizing glimpse into a future where certain tedious tasks are rendered efficient, if not effortless [1].

One of the most promising applications of generative AI lies in data synthesis and analysis. Traditional research methods often demand extensive labor for data collection, analysis, and interpretation. With generative AI, there is potential for rapid and comprehensive data synthesis. Models like ChatGPT can sift through decades of patient data swiftly, highlighting patterns and nuances that could easily be overlooked in manual analyses. Such insights not only foster a deeper understanding of existing data but also carve pathways for new inquiries.

Another area ripe for AI transformation is the vast landscape of literature reviews. With a constant influx of research publications, even the most diligent researchers struggle to stay abreast of every new development. Here, generative AI can be an invaluable ally. These models can peruse a staggering number of papers in record time, summarizing essential findings and even spotlighting potential gaps or underexplored territories in existing literature. This streamlined approach not only ensures a more comprehensive grasp of previous works but also kindles the flame of original research ideas [2].

Moreover, as the sphere of research writing and publishing evolves, generative AI can play an instrumental role in manuscript preparation. Researchers can harness AI’s prowess to refine content, enhance linguistic quality, and ensure coherence. The benefits don’t end with manuscript drafting; the peer-review process can also be enriched with AI’s touch. By scanning manuscripts, AI models can pinpoint potential conflicts of interest, methodological inconsistencies, or other pitfalls that might escape human notice.

However, the intersection of AI and cardiovascular research isn’t devoid of challenges. A looming concern is the potential over-reliance on AI, which could hamper original thought [3]. If a majority of the research community leans heavily on AI for tasks like literature review, there’s a palpable risk of homogenizing ideas, leading to an innovation drought.

Further, AI models are shaped by the data on which they are trained. Consequently, biases or inaccuracies embedded in these foundational datasets could find their way into AI-generated content. The ramifications of such misinterpretations, especially in a field as crucial as cardiovascular research, could be dire. Perhaps one of the most profound quandaries posed by the fusion of AI and research is ethical in nature. The burgeoning role of AI in manuscript writing and review beckons several pressing questions: How much AI intervention is acceptable in research? At what point does the involvement of AI dilute the authenticity of human-led research? And most pertinently, how can we strike a harmonious balance where AI augments human effort without overshadowing it?

To better understand the use and applicability of generative AI models in cardiovascular study and research, we present a holistic comparison of five most popular generative AI models such as ChatGPT, Bidirectional Encoder Representations from Transformers (BERT), Transformer, Vector Quantized Variational AutoEncoder (VQ-VAE-2), and Text-to-Text Transfer Transformer (T5). Table 1 shows the comparison of these models’ applicability in various aspects of cardiovascular research and study.

Table 1.

Comparison of various generative AI models for cardiovascular research and study

Feature/model	ChatGPT	BERT	Transformer	VQ-VAE-2	T5
Primary purpose	Text generation	Text understanding	Sequence-to-sequence tasks	Image & sound generation	Convert text inputs to outputs
Output type	Continuous text	Embedded representation	Sequence	Images & sounds	Text
Use in cardio research	Literature review, manuscript drafting	Data embedding for pattern recognition	Sequence-based data analysis	ECG or imaging representation	Converting data forms, summaries
Cardiac imaging analysis	Not optimal	Could be adapted for feature extraction	Possible sequence patterns in imaging	Direct image analysis & recreation	Text descriptors from images
Cardio genomic data handling	Speculative text-based interpretation	Genomic sequence embedding	Sequence analysis for gene patterns	Not optimal	Converting gene sequences to descriptions
Clinical trial data interpretation	Summarizing findings, patient reports	Embedding clinical data for pattern recognition	Analyzing sequence-based clinical data	Not optimal	Transforming clinical data forms
Cardiovascular study design	Drafting study parameters & initial hypothesis	Identifying similar past studies through embeddings	Analyzing sequence-based study patterns	Not directly applicable	Transforming past study designs to new formats
Ethics in cardio research	Guiding ethical considerations based on literature	Embedding ethical guidelines for pattern recognition	Sequence analysis for ethical pattern recognition	Not optimal	Converting ethical guidelines to actionable steps
Cardiovascular surgical procedures	Procedure summaries & patient care guidelines	Embedding surgical procedure steps for pattern recognition	Analyzing sequence-based surgical steps	Visualization of surgical procedures using image data	Converting surgical procedure steps to instructional format

Peering into the future, it is evident that generative AI will assume a more integrated role in cardiovascular research [4]. We can anticipate AI systems that collaborate seamlessly with researchers, suggesting experiments, analyzing live data, and even forecasting potential outcomes. Such advancements could lead to ground-breaking innovations, like virtual cardiovascular simulations, enabling researchers to test hypotheses in a controlled digital environment before any tangible application.

However, with this looming AI renaissance comes an imperative for caution. As AI intertwines more deeply with the research process, setting clear and rigorous guidelines will be paramount [5]. It falls upon research institutions, publishing houses, and AI developers to ensure these technologies amplify human potential without compromising the sacrosanct integrity of research. In concluding words, the infusion of generative AI into cardiovascular research and publishing heralds a new era teeming with possibilities. Approached with discernment and responsibility, AI can usher in efficiencies, innovations, and enhanced knowledge dissemination. But, as with any potent tool, the journey ahead demands careful navigation to ensure the promise of AI intertwines harmoniously with the values and standards of human-led research.

Author contribution

PPR: The author contributed to the conceptualisation, writing, editing, and reviewing of this editorial article.

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The author declares no competing interests.