Biomolecular Relationships Discovered from Biological Labyrinth and Lost in Ocean of Literature: Community Efforts Can Rescue Until Automated Artificial Intelligence Takes Over.
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ABSTRACT:
SUBMITTER: Gupta R
PROVIDER: S-EPMC4814459 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
Biomolecular Relationships Discovered from Biological Labyrinth and Lost in Ocean of Literature: Community Efforts Can Rescue Until Automated Artificial Intelligence Takes Over.
Gupta Rajinder R Mantri Shrikant S SS
Frontiers in genetics 20160331
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Project description:We live in interesting times. Portents of impending catastrophe pervade the literature, calling us to action in the face of unmanageable volumes of scientific data. But it isn't so much data generation per se, but the systematic burial of the knowledge embodied in those data that poses the problem: there is so much information available that we simply no longer know what we know, and finding what we want is hard - too hard. The knowledge we seek is often fragmentary and disconnected, spread thinly across thousands of databases and millions of articles in thousands of journals. The intellectual energy required to search this array of data-archives, and the time and money this wastes, has led several researchers to challenge the methods by which we traditionally commit newly acquired facts and knowledge to the scientific record. We present some of these initiatives here - a whirlwind tour of recent projects to transform scholarly publishing paradigms, culminating in Utopia and the Semantic Biochemical Journal experiment. With their promises to provide new ways of interacting with the literature, and new and more powerful tools to access and extract the knowledge sequestered within it, we ask what advances they make and what obstacles to progress still exist? We explore these questions, and, as you read on, we invite you to engage in an experiment with us, a real-time test of a new technology to rescue data from the dormant pages of published documents. We ask you, please, to read the instructions carefully. The time has come: you may turn over your papers...
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Project description:IntroductionArtificial Intelligence tools are being introduced in almost every field of human life, including medical sciences and medical education, among scepticism and enthusiasm.Research questionto assess how a generative language tool (Generative Pretrained Transformer 3.5, ChatGPT) performs at both generating questions and answering a neurosurgical residents' written exam. Namely, to assess how ChatGPT generates questions, how it answers human-generated questions, how residents answer AI-generated questions and how AI answers its self-generated question.Materials and methods50 questions were included in the written exam, 46 questions were generated by humans (senior staff members) and 4 were generated by ChatGPT. 11 participants took the exam (ChatGPT and 10 residents). Questions were both open-ended and multiple-choice.8 questions were not submitted to ChatGPT since they contained images or schematic drawings to interpret.Resultsformulating requests to ChatGPT required an iterative process to precise both questions and answers. Chat GPT scored among the lowest ranks (9/11) among all the participants). There was no difference in response rate for residents' between human-generated vs AI-generated questions that could have been attributed to less clarity of the question. ChatGPT answered correctly to all its self-generated questions.Discussion and conclusionsAI is a promising and powerful tool for medical education and for specific medical purposes, which need to be further determined. To request AI to generate logical and sound questions, that request must be formulated as precise as possible, framing the content, the type of question and its correct answers.
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