Low-power object-detection challenge on unmanned aerial vehicles

Jia, Zhenge; Xu, Xiaowei; Hu, Jingtong; Shi, Yiyu

doi:10.1038/s42256-022-00567-4

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In addition, these models offer a practical solution, enabling us to process conversation transcriptions quickly without overextending our hardware capabilities (all models were run on a single A100 NVIDIA GPU with 40 GB of VRAM), which may represent common computational resources in health care. 48 49 Furthermore, our decision is influenced by security, privacy, and compliance. Larger and more resource-intensive LLMs require application programming interface access via cloud services.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls

Sezgin,

Sirrianni,

Kranz

2024

Appl Clin Inform

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Objective: We present a proof-of-concept digital scribe system as an Emergency Department (ED) consultation call-based clinical conversation summarization pipeline to support clinical documentation, and report its performance. Materials and Methods: We use four pre-trained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records. We report the ROUGE-1, ROUGE-2, and ROUGE-L to compare model performance. In addition, we annotated transcriptions to assess the quality of generated summaries. Results: The fine-tuned BART-Large-CNN model demonstrates greater performance in summarization tasks with the highest ROUGE scores (F1ROUGE-1=0.49, F1ROUGE-2=0.23, F1ROUGE-L=0.35) scores. In contrast, PEGASUS-PubMed lags notably (F1ROUGE-1=0.28, F1ROUGE-2=0.11, F1ROUGE-L=0.22). BART-Large-CNN's performance decreases by more than 50% with the zero-shot approach. Annotations show that BART-Large-CNN performs 71.4% recall in identifying key information and a 67.7% accuracy rate. Discussion: The BART-Large-CNN model demonstrates a high level of understanding of clinical dialogue structure, indicated by its performance with and without fine-tuning. Despite some instances of high recall, there is variability in the model's performance, particularly in achieving consistent correctness, suggesting room for refinement. The model's recall ability varies across different information categories. Conclusion: The study provides evidence towards the potential of AI-assisted tools in assisting clinical documentation. Future work is suggested on expanding the research scope with additional language models and hybrid approaches, and comparative analysis to measure documentation burden and human factors.

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Section: Methodsmentioning

confidence: 99%

Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls

Sezgin,

Sirrianni,

Kranz

2024

Appl Clin Inform

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“…We introduced the article format of Challenge Accepted in Nature Machine Intelligence to highlight these benefits in short, accessible articles that are written by organizers, winners or runners-up. For instance, a recent Challenge Accepted reported on a competition in object detection with aerial vehicles and also combined several metrics, rewarding efficient and practical solutions rather than ones that narrowly focused on achieving high accuracy 6 . We welcome proposals for contributions to this section of the journal highlighting competitions that are similarly forward-looking and stimulating for the community.…”

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confidence: 99%

Algorithmic recommendations, anyone?

2023

Nat Mach Intell

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“…Meanwhile, resource sustainability issues are also becoming a concern in applying AI in drug discovery. 14,15 Therefore, a revolution is needed not only in data utilization but also in methodological design.…”

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confidence: 99%