The importance of resource awareness in artificial intelligence for healthcare

Jia, Zhenge; Chen, Jianxu; Xu, Xiaowei; Kheir, John N.; Hu, Jingtong; Han, Xiao; Peng, Sancheng; Hu, Xiaobo Sharon; Chen, Danny; Shi, Yiyu

doi:10.1038/s42256-023-00670-0

Cited by 28 publications

(6 citation statements)

References 109 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 40GB of VRAM), which may represent common computational resources in healthcare. [42,43] Furthermore, our decision is influenced by security, privacy, and compliance. Larger and more resource-intensive LLMs require API access via cloud services.…”

Section: Methodsmentioning

confidence: 99%

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Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden

Sezgin,

Sirrianni,

Kranz

2023

Preprint

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ObjectiveWe present a proof-of-concept digital scribe system as an ED clinical conversation summarization pipeline and report its performance.Materials and MethodsWe 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.ResultsThe 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.DiscussionThe 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.ConclusionThe study provides evidence towards the potential of AI-assisted tools in reducing clinical documentation burden. Future work is suggested on expanding the research scope with larger language models, and comparative analysis to measure documentation efforts and time.

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Section: Methodsmentioning

confidence: 99%

Section: Model Selectionmentioning

confidence: 99%

Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden

Sezgin,

Sirrianni,

Kranz

2023

Preprint

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“…Additional hurdles to the equitable rollout of AI in the assessment and treatment of aphasia relate to technological costs and availability. To fuel the training and implementation of such a complex AI-driven healthcare application, a substantial expense budget must be available to grow the computational power of hardware, particularly to increase the number of graphics processing units (GPUs) to handle the complexity of these AI models and to handle the real-time processing of large amounts of data [93]. Supporting remote diagnosis necessitates the availability of a high-speed network communication infrastructure for real-time data transmission between devices and servers.…”

Section: Practical Considerations In the Equitable Rollout Of Aimentioning

confidence: 99%

AI and Aphasia in the Digital Age: A Critical Review

Privitera,

Ng,

Kong

et al. 2024

Brain Sciences

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Aphasiology has a long and rich tradition of contributing to understanding how culture, language, and social environment contribute to brain development and function. Recent breakthroughs in AI can transform the role of aphasiology in the digital age by leveraging speech data in all languages to model how damage to specific brain regions impacts linguistic universals such as grammar. These tools, including generative AI (ChatGPT) and natural language processing (NLP) models, could also inform practitioners working with clinical populations in the assessment and treatment of aphasia using AI-based interventions such as personalized therapy and adaptive platforms. Although these possibilities have generated enthusiasm in aphasiology, a rigorous interrogation of their limitations is necessary before AI is integrated into practice. We explain the history and first principles of reciprocity between AI and aphasiology, highlighting how lesioning neural networks opened the black box of cognitive neurolinguistic processing. We then argue that when more data from aphasia across languages become digitized and available online, deep learning will reveal hitherto unreported patterns of language processing of theoretical interest for aphasiologists. We also anticipate some problems using AI, including language biases, cultural, ethical, and scientific limitations, a misrepresentation of marginalized languages, and a lack of rigorous validation of tools. However, as these challenges are met with better governance, AI could have an equitable impact.

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“…There is a need to address the demand for efficient models in histological imaging and medical imaging in general, to enable performant models using limited resources [18]. This can contribute to more cost-effective solutions and wider accessibility especially in resource-limited healthcare facilities that suffer from insufficient infrastructure to support large models and a shortage of medical specialists.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Histological Tumor Classification Using a Joint Sparsity-Quantization Aware Training Framework

Aboutahoun,

Zewail,

Kimura

et al. 2023

IEEE Access

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Cancer decision-making is a complex process that can be exacerbated by the limited availability of oncological expertise. This is particularly true in rural areas and settings with fewer resources. Recently, there has been an interest in the potential of artificial intelligence in reliable computer-aided diagnosis tools in such settings. Nevertheless, the majority of deep learning algorithms are resource hungry in terms of data and storage requirements. In this work, we propose a novel lightweight deep learning model for histological tumor classification through a Joint Sparsity-Quantization Aware Training framework. Extensive experiments were conducted to evaluate the proposed framework. This work aims at opening doors toward efficient point-of-care diagnostic devices suitable for environments with limited resources.

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The importance of resource awareness in artificial intelligence for healthcare

Cited by 28 publications

References 109 publications

Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden

Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden

AI and Aphasia in the Digital Age: A Critical Review

Lightweight Histological Tumor Classification Using a Joint Sparsity-Quantization Aware Training Framework

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