Opportunities and challenges for ChatGPT and large language models in biomedicine and health

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 25(1)

Published: Nov. 22, 2023

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This subsequently led to emergence of diverse applications in field biomedicine health. In this work, we examine large language models (LLMs), such as ChatGPT, Specifically explore areas biomedical information retrieval, question answering, medical summarization, extraction, education, investigate whether LLMs possess transformative power revolutionize these tasks or distinct complexities presents unique challenges. Following an extensive literature survey, find that significant advances have been made tasks, surpassing previous state-of-the-art methods. For other applications, modest. Overall, not yet revolutionized biomedicine, but recent rapid progress indicates methods hold great potential provide valuable means for accelerating discovery improving We also use LLMs, like fields health entails various risks challenges, including fabricated generated responses, well legal privacy concerns associated sensitive patient data. believe survey can a comprehensive timely overview researchers healthcare practitioners on opportunities challenges using transforming

Language: Английский

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ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

International Journal of Oral Science, Journal Year: 2023, Volume and Issue: 15(1)

Published: July 28, 2023

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one the milestone Large Language Models (LLMs) with billions parameters. LLMs have stirred up much interest among researchers practitioners in their impressive skills natural language processing tasks, which profoundly impact various fields. This paper mainly discusses future applications dentistry. We introduce two primary LLM deployment methods dentistry, including automated dental diagnosis cross-modal diagnosis, examine potential applications. Especially, equipped encoder, single can manage multi-source data conduct advanced reasoning to perform complex clinical operations. also present cases demonstrate fully automatic Multi-Modal AI system for dentistry application. While offer significant benefits, challenges, such as privacy, quality, model bias, need further study. Overall, revolutionize treatment, indicates promising avenue application research

Language: Английский

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The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D938 - D949

Published: Nov. 24, 2023

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing ontologies, semantic models, knowledge graphs translational research. App an integrated platform combining about genes, phenotypes, diseases across species. Monarch's APIs enable access to carefully curated datasets advanced analysis tools that support disease diverse applications such as variant prioritization, deep phenotyping, patient profile-matching. We have migrated our system into scalable, cloud-based infrastructure; simplified ingestion graph integration systems; enhanced mapping standards; developed new user interface with novel search navigation features. Furthermore, we analytic customized plugin OpenAI's ChatGPT increase reliability its responses data, allowing us interrogate in using state-of-the-art Large Language Models. resources can be found monarchinitiative.org corresponding code repository github.com/monarch-initiative/monarch-app.

Language: Английский

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On the limitations of large language models in clinical diagnosis

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 14, 2023

Abstract Objective Large Language Models such as GPT-4 previously have been applied to differential diagnostic challenges based on published case reports. Published reports a sophisticated narrative style that is not readily available from typical electronic health records (EHR). Furthermore, even if were in EHRs, privacy requirements would preclude sending it outside the hospital firewall. We therefore tested method for parsing clinical texts extract ontology terms and programmatically generating prompts by design are free of protected information. Materials Methods investigated different methods prepare 75 recently transformed original narratives extracting structured representing phenotypic abnormalities, comorbidities, treatments, laboratory tests creating programmatically. Results Performance all these approaches was modest, with correct diagnosis ranked first only 5.3-17.6% cases. The performance created data substantially worse than texts, additional information added following manual review term extraction. Moreover, versions demonstrated this task. Discussion sensitivity form prompt instability results over two represent important current limitations use support real-life settings. Conclusion Research needed identify best typically diagnostics.

Language: Английский

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Evaluation of large language models for discovery of gene set function

Nature Methods, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 28, 2024

Language: Английский

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Construction of Knowledge Graphs: Current State and Challenges

Information, Journal Year: 2024, Volume and Issue: 15(8), P. 509 - 509

Published: Aug. 22, 2024

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, need for generalized pipelines to construct continuously update KGs is increasing. While individual steps that are necessary create from unstructured sources (e.g., text) structured data databases) mostly well researched their one-shot execution, adoption incremental KG updates interplay have hardly been investigated in a systematic manner so far. In this work, we first discuss main graph models introduce major requirements future construction pipelines. Next, provide an overview build high-quality KGs, including cross-cutting topics metadata management, ontology development, quality assurance. We then evaluate state art with respect introduced specific popular some recent tools strategies construction. Finally, identify areas further research improvement.

Language: Английский

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Can LLMs Generate Competency Questions?

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 71 - 80

Published: Jan. 1, 2025

Language: Английский

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LLM-Based Guided Generation of Ontology Term Definitions

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 133 - 137

Published: Jan. 1, 2025

Language: Английский

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Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

Journal of Biomedical Semantics, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 16, 2024

Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge an accurate computable form. However, their construction maintenance demand substantial resources necessitate collaboration between domain experts, curators, ontology experts. We present Dynamic Retrieval Augmented Generation using AI (DRAGON-AI), generation method employing Large Language Models (LLMs) (RAG). DRAGON-AI can generate textual logical components, drawing from existing multiple ontologies unstructured text sources.

Language: Английский

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Ontology engineering with Large Language Models

Published: Sept. 11, 2023

We tackle the task of enriching ontologies by automatically translating natural language (NL) into Description Logic (DL). Since Large Language Models (LLMs) are best tools for translations, we fine-tuned a GPT-3 model to convert NL OWL Functional Syntax. For fine-tuning, designed pairs sentences in and corresponding translations. This training cover various aspects from ontology engineering: instances, class subsumption, domain range relations, object properties relationships, disjoint classes, complements, or cardinality restrictions. The resulted axioms used enrich an ontology, human supervised manner. developed tool is publicly provided as Protégé plugin.

Language: Английский

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