Bridging artificial intelligence in medicine with generative pre-trained transformer (GPT) technology

Waisberg, Ethan; Ong, Joshua; Kamran, Sharif Amit; Masalkhi, Mouayad; Zaman, Nasif; Sarker, Prithul; Lee, Andrew G.; Tavakkoli, Alireza

doi:10.21037/jmai-23-36

Cited by 11 publications

(6 citation statements)

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“…The paper highlights ChatGPT's potential in democratizing coding and developing AI in medicine, leading to breakthroughs in the medical AI sector [20]. The focus on ethical concerns, patient autonomy, and the responsible use of AI in medicine, along with the exploration of AI's potential to revolutionize medical research and practice, aligns with this category [20]. These existing research works could be categorized into six distinct categores, as described in Figure 2.…”

Section: Literature Reviewmentioning

confidence: 97%

“…It examines ChatGPT's ability to develop AI programs for medicine, its limitations and challenges, ethical concerns like biases and patient confidentiality, and compliance with healthcare regulations. The paper highlights ChatGPT's potential in democratizing coding and developing AI in medicine, leading to breakthroughs in the medical AI sector [20]. The focus on ethical concerns, patient autonomy, and the responsible use of AI in medicine, along with the exploration of AI's potential to revolutionize medical research and practice, aligns with this category [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It highlights the application of AI models in creating synthetic medical text data, aiming to augment existing datasets for improved model training and performance in specific medical tasks [9]. Finally, the paper in [20] discusses the potential of ChatGPT in various medical applications. It examines ChatGPT's ability to develop AI programs for medicine, its limitations and challenges, ethical concerns like biases and patient confidentiality, and compliance with healthcare regulations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Ethical and Public Health Implications of AI in Medicine: Several recent studies like [11,[14][15][16]18,20] discuss the broader ethical implications and public health concerns of AI in medicine, including misinformation and academic integrity.…”

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confidence: 99%

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Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Sufi

2024

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This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts.

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Section: Literature Reviewmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Sufi

2024

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“…The paper "Investigating Paraphrasing-Based Data Augmentation for Task-Oriented Dialogue Systems" by Liane Vogel and Lucie Flek explores data augmentation in taskoriented dialogue systems using paraphrasing techniques with GPT-2 and Conditional Variational Autoencoder (CVAE) models [70]. The study demonstrates how these models can effectively generate paraphrased template phrases, significantly reducing the need for manually annotated training data while maintaining or even improving the performance of a natural language understanding (NLU) system [70]. Shuohua Zhou and Yanping Zhang focus on improving medical question-answering systems [71].…”

Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%

Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Sufi

2024

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GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits.

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Understanding natural language: Potential application of large language models to ophthalmology

Yang,

Wang,

Zhou

et al. 2024

Asia-Pacific Journal of Ophthalmology

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Bridging artificial intelligence in medicine with generative pre-trained transformer (GPT) technology

Cited by 11 publications

References 0 publications

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Understanding natural language: Potential application of large language models to ophthalmology

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