Artificial Intelligence in Radiotherapy and Patient Care

Chow, James C. L.

doi:10.1007/978-3-030-58080-3_143-1

Cited by 10 publications

(6 citation statements)

References 35 publications

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“…This also aroused great interest among researchers and led to many related advances in the field of medicine [17]. Moreover, DNNs have made significant breakthroughs in radiation therapy [18,19], including treatment outcome prediction [20] and treatment optimization [21,22] et al However, there are few DNN studies on QA, and the success of DNN methods in radiation therapy shows its strong potential in QA.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy

Xie,

Zhang,

et al. 2024

Bioengineering

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Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples’ model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

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Section: Related Workmentioning

confidence: 99%

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy

Xie,

Zhang,

et al. 2024

Bioengineering

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“…The incorporation of LLMs into medical chatbots introduces significant advantages, revolutionizing the healthcare interactions. LLMs enhance the understanding of contextual nuances in medical queries, enabling chatbots to provide more accurate and relevant responses [52]. This heightened comprehension fosters a humanized interaction, with LLMs proficiently mimicking natural language patterns, creating a more engaging and empathetic user experience.…”

Section: Evolution Of Large Language Modelsmentioning

confidence: 99%

Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

Chow,

Wong,

2024

BioMedInformatics

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This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.

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“…Chatbot creators can build the underlying software, link it to a maintained database, and easily modify the conversational flow and data. In contrast, ChatGPT was not trained by a specialized team of medical professionals (Chow, 2021 ; Siddique and Chow, 2021 ). Its strength lies in its natural language processing (NLP) capabilities, based on the Generative Pre-trained Transformer (GPT-3.5) form of AI.…”

Section: Some Obstacles On the Path To Chatgpt Becoming A Medical Cha...mentioning

confidence: 99%

Impact of ChatGPT on medical chatbots as a disruptive technology

Chow

Sanders

2023

Front. Artif. Intell.

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Artificial Intelligence in Radiotherapy and Patient Care

Cited by 10 publications

References 35 publications

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy

Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

Impact of ChatGPT on medical chatbots as a disruptive technology

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