Painful conversations: Therapeutic chatbots and public capacities

Yang, Misti

doi:10.1177/2057047320950636

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Dermatology, which is an image and screening intensive subfield of medicine, has great synergy with deep learning techniques pertaining to image processing [3][4][5][6]. Automation is carried out in electronic health records [7], therapeutic chatbots [8][9][10], and health monitoring.…”

Section: Introductionmentioning

confidence: 99%

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Acharya

Dhiman

Prakash

et al. 2022

Computational Intelligence and Neuroscience

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Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.

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Section: Introductionmentioning

confidence: 99%

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Acharya

Dhiman

Prakash

et al. 2022

Computational Intelligence and Neuroscience

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“…Robots and telemedicine are being used to keep health workers safe during the COVID-19 pandemic (14). And people with mental health issues can use therapeutic chatbots (15). AI offers a broad range of possibilities in both academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Literature analysis of artificial intelligence in biomedicine

Hulsen¹

2022

Ann Transl Med

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Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications.Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI;China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

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“…Robots and telemedicine are being used to keep health workers safe during the COVID-19 pandemic [9]. And people with mental health issues can use therapeutic chatbots [10]. AI offers a broad range of possibilities in both academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Literature Analysis of Artificial Intelligence in Biomedicine

Hulsen¹

2021

Preprint

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Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or other applications. Therefore, both academia and industry are spending a lot of time and money in AI. This review takes a look at the biomedical literature (in the PubMed and Embase databases) and does some interesting observations: AI is growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust. In order for AI to succeed in making healthcare better, it should be fully integrated into the clinician’s workflow so that they can focus on the contact with their patients.

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Painful conversations: Therapeutic chatbots and public capacities

Cited by 9 publications

References 21 publications

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Literature analysis of artificial intelligence in biomedicine

Literature Analysis of Artificial Intelligence in Biomedicine

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