Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?

Alberts, Ian; Mercolli, Lorenzo; Pyka, Thomas; Prenosil, George; Shi, Kuangyu; Rominger, Axel; Afshar‐Oromieh, Ali

doi:10.1007/s00259-023-06172-w

Cited by 75 publications

(56 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, based on the test results, [28] also reached the conclusion that ChatGPT fares better on 'memorization (fact-lookup)' type questions than on those requiring critical thinking, similar to [83]. The performance of ChatGPT on these question-answering scenarios 3 shows its potential for medical consultation and education.…”

Section: Reviews Of Level 3 Papersmentioning

confidence: 66%

“…It is not unexpected that the majority of shortlisted papers fall into the Level 1 and Level 2 category. As seen from Table 1, most of Level 1 and Level 2 papers are short editorial comments or letters to the editor from multidisciplinary journals like Nature (https://www.nature.com/) and Science (https://www.science.org/), or speciality journals like nuclear medicine [3, 59], plastic surgery [79, 38], syn-thetic biology [106] and orthopaedic [81]. These publications usually deliver high-level comments about the potential impact and pitfalls of ChatGPT in healthcare [113], with a focus on medical publishing.…”

Section: Methodsmentioning

confidence: 99%

“…ChatGPT developers revealed that future versions of ChatGPT will have vision capabilities, and can comprehend images 3. Exams are essentially also question-answering.…”

mentioning

confidence: 99%

See 2 more Smart Citations

ChatGPT in Healthcare: A Taxonomy and Systematic Review

Li¹,

Amin²,

Kleesiek³

et al. 2023

Preprint

View full text Add to dashboard Cite

The recent release of ChatGPT, a chat bot research project/product of natural language processing (NLP) by OpenAI, stirs up a sensation among both the general public and medical professionals, amassing a phenomenally large user base in a short time. This is a typical example of the 'productization' of cutting-edge technologies, which allows the general public without a technical background to gain firsthand experience in artificial intelligence (AI), similar to the AI hype created by AlphaGo (DeepMind Technologies, UK) and self-driving cars (Google, Tesla, etc.). However, it is crucial, especially for healthcare researchers, to remain prudent amidst the hype. This work provides a systematic review of existing publications on the use of ChatGPT in healthcare, elucidating the 'status quo' of ChatGPT in medical applications, for general readers, healthcare professionals as well as NLP scientists. The large biomedical literature databasePubMedis used to retrieve published works on this topic using the keyword 'ChatGPT'. An inclusion criterion and a taxonomy are further proposed to filter the search results and categorize the selected publications, respectively. It is found through the review that the current release of ChatGPT has achieved only moderate or 'passing' performance in a variety of tests, and is unreliable for actual clinical deployment, since it is not intended for clinical applications by design. We conclude that specialized NLP models trained on (bio)medical datasets still represent the right direction to pursue for critical clinical applications.

show abstract

Section: Reviews Of Level 3 Papersmentioning

confidence: 66%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

ChatGPT in Healthcare: A Taxonomy and Systematic Review

Li¹,

Amin²,

Kleesiek³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the area of health care education, ChatGPT appears to have a massive transformative potential. The need to rethink and revise the current assessment tools in health care education comes in light of ChatGPT’s ability to pass reputable exams (e.g., USMLE) and possibility of ChatGPT misuse, which would result in academic dishonesty [ 24 , 34 , 58 , 59 , 62 , 76 , 85 , 86 , 87 ].…”

Section: Discussionmentioning

confidence: 99%

ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

2023

View full text Add to dashboard Cite

ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.

show abstract

“…In recent years, large language models such as ChatGPT [4] , llama [9] , etc. have shown a high level of intelligence in dialogue Q&A, etc.…”

Section: Large Language Modelsmentioning

confidence: 99%

Research on grid inspection technology based on general knowledge enhanced multimodal large language models

Gao,

rao,

gao

et al. 2024

MIPPR 2023: Pattern Recognition and Computer Vision

View full text Add to dashboard Cite

Currently, object detection based on deep learning has received extensive research and attention in the field of grid inspection, achieving high detection accuracy and recognition precision. However, pre-trained object detection models lack overall perception and reasoning capabilities, resulting in higher false positives and missings due to a lack of holistic understanding of challenging samples. Recently, the combination of natural language models and image understanding in multi-modal large language models has gained significant attention. In this paper, we propose the Grid-Blip model, a multi-modal large model enhanced with general knowledge, to specifically study wildfires detection in grid inspection. Grid-Blip is based on the blip model architecture, which includes a natural language model, a visual generation model, and a fusion model. We conduct large-scale sample annotation at the semantic level of whole-image grid inspection, providing crucial training samples for multi-modal large-scale model research. Furthermore, we investigate the design of the fusion model network, training the model to effectively integrate the pre-trained natural language model and visual generation model. Experimental results demonstrate that compared to object detection models, the proposed multi-modal large-scale model in this paper achieves overall semantic perception and reasoning capabilities. The Grid-Blip model reduces the false alarm rate for wildfire smoke trend prediction from 20% to 10% and the missed detection rate from 18% to 13%.

show abstract

Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?

Cited by 75 publications

References 30 publications

ChatGPT in Healthcare: A Taxonomy and Systematic Review

ChatGPT in Healthcare: A Taxonomy and Systematic Review

ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

Research on grid inspection technology based on general knowledge enhanced multimodal large language models

Contact Info

Product

Resources

About