Deep learning for Arabic healthcare: MedicalBot

Abdelhay, Mohammed; Mohammed, Ammar; Hefny, Hesham A.

doi:10.1007/s13278-023-01077-w

Cited by 29 publications

(3 citation statements)

References 42 publications

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“…Recent studies [52,53] observed that Arabic language chatbots driven by the latest technologies using AI and NLP are relatively scarce largely due to the complex nature of the Arabic language, and the AI virtual assistants are still lagging in applying the Arabic language in AIbased assistant development in the fields of mental health. However, with the adoption of innovative techniques such as deep learning, recent studies [54,55] have observed greater accuracy of the Arabic language in virtual assistants, indicating the increased ease of use and understandability of information provided by the assistants.…”

Section: Discussionmentioning

confidence: 99%

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

Alanzi,

Alsalem,

Alzahrani

et al. 2023

Cureus

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Study purpose: This study aims to analyze various influencing factors among generations X (Gen X), Y (Gen Y), and Z (Gen Z) of artificial intelligence (AI)-powered mental health virtual assistants. Methods: A cross-sectional survey design was adopted in this study. The study sample consisted of outpatients diagnosed with various mental health illnesses, such as anxiety, depression, schizophrenia, and behavioral disorders. A survey questionnaire was designed based on the factors (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention) identified from the unified theory of acceptance and use of the technology model. Ethical approval was received from the Ethics Committee at Imam Abdulrahman Bin Faisal University, Saudi Arabia. Results: A total of 506 patients participated in the study, with over 80% having moderate to high experience in using mental health AI assistants. The ANOVA results for performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioral intentions (BI) indicate that there are statistically significant differences (p < 0.05) between the Gen X, Gen Y, and Gen Z participants. Conclusion: The findings underscore the significance of considering generational differences in attitudes and perceptions, with Gen Y and Gen Z demonstrating more positive attitudes and stronger intentions to use AI mental health virtual assistants, while Gen X appears to be more cautious.

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Section: Discussionmentioning

confidence: 99%

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

Alanzi,

Alsalem,

Alzahrani

et al. 2023

Cureus

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“…These chatbots utilize neural networks and natural language processing to provide personalized healthcare information and advice to users. They can answer user queries related to various health issues, ranging from psychological counseling to domain-specific medical knowledge [16,17]. These advancements in medical-questionanswering chatbots have the potential to revolutionize healthcare delivery and improve access to information for patients.…”

Section: Medical Qandamentioning

confidence: 99%

Towards Reliable Healthcare LLM Agents: A Case Study for Pilgrims during Hajj

Alghamdi,

Mostafa

2024

Information

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There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided by these healthcare agents, particularly in critical scenarios such as medical emergencies. Pilgrims come from diverse cultural and linguistic backgrounds, often facing difficulties in accessing medical advice and information. Establishing an AI-powered multilingual chatbot can bridge this gap by providing readily available medical guidance and support, contributing to the well-being and safety of pilgrims. In this paper, we present a comprehensive methodology aimed at enhancing the reliability and efficacy of healthcare conversational agents, with a specific focus on addressing the needs of Hajj pilgrims. Our approach leverages domain-specific fine-tuning techniques on a large language model, alongside synthetic data augmentation strategies, to optimize performance in delivering contextually relevant healthcare information by introducing the HajjHealthQA dataset. Additionally, we employ a retrieval-augmented generation (RAG) module as a crucial component to validate uncertain generated responses, which improves model performance by 5%. Moreover, we train a secondary AI agent on a well-known health fact-checking dataset and use it to validate medical information in the generated responses. Our approach significantly elevates the chatbot’s accuracy, demonstrating its adaptability to a wide range of pilgrim queries. We evaluate the chatbot’s performance using quantitative and qualitative metrics, highlighting its proficiency in generating accurate responses and achieve competitive results compared to state-of-the-art models, in addition to mitigating the risk of misinformation and providing users with trustworthy health information.

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“…The deep-learning-based object detection method improves on the main problems of the sliding window strategy in traditional object detection methods as well as the poor robustness of manually designed features. Due to the excellent performance of deep-learning-based object detection methods, they are popular in a wide range of industries and are widely used in areas such as intelligent transportation [16], aerospace [17], health care [18], and industrial detection [19]. However, the use of deep learning methods on substation inspection robots and the detection of various types of power equipment defects based on deep learning are relatively little researched.…”

Section: Introductionmentioning

confidence: 99%

A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN

Yang,

Ma,

Guo

et al. 2023

Sensors

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Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.

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Deep learning for Arabic healthcare: MedicalBot

Cited by 29 publications

References 42 publications

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

Towards Reliable Healthcare LLM Agents: A Case Study for Pilgrims during Hajj

A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN

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