The role of artificial intelligence in generating original scientific research

Elbadawi, Moe; Li, Hanxiang; Basit, Abdul W.; Gaisford, Simon

doi:10.1016/j.ijpharm.2023.123741

Cited by 5 publications

(18 citation statements)

References 62 publications

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“…The paper in [3] explores the potential of AI, particularly large language models (LLMs) like GPT-4, in generating original scientific research. It discusses the use of GPT-4 to write an original pharmaceutics manuscript, including formulating a research hypothesis, defining an experimental protocol, producing photo-realistic images, generating analytical data, and writing a publication-ready manuscript.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It discusses the use of GPT-4 to write an original pharmaceutics manuscript, including formulating a research hypothesis, defining an experimental protocol, producing photo-realistic images, generating analytical data, and writing a publication-ready manuscript. This study also examines the limitations of LLMs in referencing literature and emphasizes the need for human input in interpretation and data validation [3]. It focuses on the innovative use of LLMs to generate and augment data, such as creating believable analytical data and images for pharmaceutical research [3].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unlike previous studies that relied on manual utilization of GPT's web interface (as shown in [3][4][5][6][7][8][9]), this research autonomously leveraged the GPT Application This study contributes to the current body of knowledge in the following ways:…”

mentioning

confidence: 99%

“…• Conducted a comprehensive review of existing literature to explore the utilization of GPT in the medical domain. Among twenty identified works, this study highlighted seven distinct research endeavors that employed GPT to generate or enhance medically relevant data [3][4][5][6][7][8][9].…”

mentioning

confidence: 99%

“…Unlike previous studies that relied on manual utilization of GPT's web interface (as shown in [3][4][5][6][7][8][9]), this research autonomously leveraged the GPT Application Programming Interface (API) alongside automation tools, enabling the efficient generation of a large volume of medically significant data.…”

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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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: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Unlike previous studies that relied on manual utilization of GPT's web interface (as shown in [3][4][5][6][7][8][9]), this research autonomously leveraged the GPT Application Programming Interface (API) alongside automation tools, enabling the efficient generation of a large volume of medically significant data.…”

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 Role of AI in Drug Discovery

Abbas,

Rassam,

Karamshahi

et al. 2024

ChemBioChem

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The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

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Artificial intelligence (and related topics, e.g., machine learning, deep learning, artificial neural networks or ANNs) as applied to the teaching and to the practice of analytical spectrochemistry

Tat,

Karanassios

2024

Next-Generation Spectroscopic Technologies XVI

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In this paper, the application of Artificial Intelligence (AI) and related topics (e.g., Machine Learning, Artificial Neural Networks (ANNs), deep learning) as they apply to analytic spectrometry (e.g., either using Inductively Coupled Plasma -Optical Emission Spectrometry (ICP-OES), or a portable, battery-operated microplasma-OES) using a fiber-optic spectrometer) will be described, and the application of AI to teaching analytical atomic spectrometry will be outlined.

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The role of artificial intelligence in generating original scientific research

Cited by 5 publications

References 62 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

The Role of AI in Drug Discovery

Artificial intelligence (and related topics, e.g., machine learning, deep learning, artificial neural networks or ANNs) as applied to the teaching and to the practice of analytical spectrochemistry

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