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Artificial Intelligence Technologies for Healthcare

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 29344

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Special Issue Editors

Dr. Massimo Esposito

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Guest Editor

Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), 80131 Naples, Italy
Interests: artificial Intelligence; natural language processing; knowledge engineering; conversational systems
Special Issues, Collections and Topics in MDPI journals

Dr. Giuseppe De Pietro

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Guest Editor

National Research Council of Italy (CNR)—Institute for High Performance Computing & Networking (ICAR), Via P Castellino 111, 80131 Naples, Italy
Interests: decision support systems; pervasive computing; e-health
Special Issues, Collections and Topics in MDPI journals

Dr. Giovanni Luca Masala

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Guest Editor

School of Computing, University of Kent, Canterbury CT2 7NZ, UK
Interests: artificial intelligence; machine learning; medical imaging; digital healthcare; social robots in healthcare; assistive robotics; cognitive systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, digital services are driving the technological revolution in all sectors of our society. In particular, there is continuous research into new services in medical disciplines powered by cutting-edge computational technologies such as artificial intelligence. Although digital health technology still involves novice applications, researchers seek to reach more precise interventions, improved health outcomes, increased efficiency, and ultimately reduced healthcare expenditure (Inkster et al, 2019).

The growing interest in artificial intelligence technologies and the availability of big data analysis techniques have introduced the potential use of aggregated healthcare data to produce powerful models that can automate diagnoses (Panch et al, 2019; Keane et al, 2018) and enable the increasing precision approach to medicine by tailoring treatments and targeting resources with maximum effectiveness in a timely and dynamic manner (Shaban-Nejad, 2018; Fogel et al, 2018). Therefore, there is interest in creating new eHealth architectures powered by AI. To fulfil its potential across health systems, advanced research is necessary in medical topics from data analyses of medical data to the diagnosis and detection of diseases. Furthermore, “special needs” groups, like clinical populations, deprived communities, children, and seniors, require other kinds of AI services involving intelligent and conversational agents that support mental and psychiatric disorders (dementia, autism, and depression), social robots in elderly care, and assistive robotics for physical rehabilitation.

The application of AI methods and technologies in papers addressing these topics are invited for this Special Issue, especially those combining a high academic standard coupled with a practical focus on providing optimal technologies for healthcare.

References

Inkster, Becky et al. "Improving insights into health care with data linkage to financial technology." The Lancet Digital Health 1.3 (2019): e110-e112.

Keane, P. A. & Topol, E. J. With an eye to AI and autonomous diagnosis. NPJ Digit Med 1, 40 (2018).

Shaban-Nejad, A., Michalowski, M. & Buckeridge, D. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit Med. 1, 53 (2018).

Fogel, A. L. & Kvedar, J. C. Artificial intelligence powers digital medicine. NPJ Digit Med. 1, 5 (2018).

Panch, T., Mattie, H. & Celi, L.A. The “inconvenient truth” about AI in healthcare. npj Digit. Med. 2, 77 (2019). https://doi.org/10.1038/s41746-019-0155-4

Keywords

statistical machine learning in medicine
neural networks and deep learning in medical imaging
computer aided detection/diagnosis systems in radiology
modelling virus evolution
decision support based on electronic health records
explainable AI in eHealth
intelligent and conversational agents that support mental and psychiatric disorders (dementia
autism and depression)
social robots in elderly care
assistive robotics for physical rehabilitation and silver care
eHealth architectures powered by AI

Published Papers (6 papers)

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Research

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10 pages, 1534 KiB

Open AccessArticle

Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study

by Takanobu Hirosawa, Yukinori Harada, Masashi Yokose, Tetsu Sakamoto, Ren Kawamura and Taro Shimizu

Int. J. Environ. Res. Public Health 2023, 20(4), 3378; https://doi.org/10.3390/ijerph20043378 - 15 Feb 2023

Cited by 104 | Viewed by 8808

Abstract

The diagnostic accuracy of differential diagnoses generated by artificial intelligence (AI) chatbots, including the generative pretrained transformer 3 (GPT-3) chatbot (ChatGPT-3) is unknown. This study evaluated the accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical vignettes with common chief complaints. General internal medicine physicians created clinical cases, correct diagnoses, and five differential diagnoses for ten common chief complaints. The rate of correct diagnosis by ChatGPT-3 within the ten differential-diagnosis lists was 28/30 (93.3%). The rate of correct diagnosis by physicians was still superior to that by ChatGPT-3 within the five differential-diagnosis lists (98.3% vs. 83.3%, p = 0.03). The rate of correct diagnosis by physicians was also superior to that by ChatGPT-3 in the top diagnosis (53.3% vs. 93.3%, p < 0.001). The rate of consistent differential diagnoses among physicians within the ten differential-diagnosis lists generated by ChatGPT-3 was 62/88 (70.5%). In summary, this study demonstrates the high diagnostic accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical cases with common chief complaints. This suggests that AI chatbots such as ChatGPT-3 can generate a well-differentiated diagnosis list for common chief complaints. However, the order of these lists can be improved in the future. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)

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15 pages, 1640 KiB

Open AccessArticle

Classification of Depression and Its Severity Based on Multiple Audio Features Using a Graphical Convolutional Neural Network

by Momoko Ishimaru, Yoshifumi Okada, Ryunosuke Uchiyama, Ryo Horiguchi and Itsuki Toyoshima

Int. J. Environ. Res. Public Health 2023, 20(2), 1588; https://doi.org/10.3390/ijerph20021588 - 15 Jan 2023

Viewed by 1556

Abstract

Audio features are physical features that reflect single or complex coordinated movements in the vocal organs. Hence, in speech-based automatic depression classification, it is critical to consider the relationship among audio features. Here, we propose a deep learning-based classification model for discriminating depression and its severity using correlation among audio features. This model represents the correlation between audio features as graph structures and learns speech characteristics using a graph convolutional neural network. We conducted classification experiments in which the same subjects were allowed to be included in both the training and test data (Setting 1) and the subjects in the training and test data were completely separated (Setting 2). The results showed that the classification accuracy in Setting 1 significantly outperformed existing state-of-the-art methods, whereas that in Setting 2, which has not been presented in existing studies, was much lower than in Setting 1. We conclude that the proposed model is an effective tool for discriminating recurring patients and their severities, but it is difficult to detect new depressed patients. For practical application of the model, depression-specific speech regions appearing locally rather than the entire speech of depressed patients should be detected and assigned the appropriate class labels. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)

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13 pages, 2827 KiB

Open AccessArticle

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication

by Rasheed Omobolaji Alabi, Alhadi Almangush, Mohammed Elmusrati, Ilmo Leivo and Antti Mäkitie

Int. J. Environ. Res. Public Health 2022, 19(14), 8366; https://doi.org/10.3390/ijerph19148366 - 08 Jul 2022

Cited by 9 | Viewed by 1960

Abstract

Background: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges. An instance of the concerns inherent to the science of machine learning is explainability. Objectives: This study measures the usability and explainability of a machine learning-based web prognostic tool that was designed for prediction of oral tongue cancer. We used the System Usability Scale (SUS) and System Causability Scale (SCS) to evaluate the explainability of the prognostic tool. In addition, we propose a framework for the evaluation of post hoc explainability of web-based prognostic tools. Methods: A SUS- and SCS-based questionnaire was administered amongst pathologists, radiologists, cancer and machine learning researchers and surgeons (n = 11) to evaluate the quality of explanations offered by the machine learning-based web prognostic tool to address the concern of explainability and usability of these models for cancer management. The examined web-based tool was developed by our group and is freely available online. Results: In terms of the usability of the web-based tool using the SUS, 81.9% (45.5% strongly agreed; 36.4% agreed) agreed that neither the support of a technical assistant nor a need to learn many things were required to use the web-based tool. Furthermore, 81.8% agreed that the evaluated web-based tool was not cumbersome to use (usability). The average score for the SCS (explainability) was 0.74. A total of 91.0% of the participants strongly agreed that the web-based tool can assist in clinical decision-making. These scores indicated that the examined web-based tool offers a significant level of usability and explanations about the outcome of interest. Conclusions: Integrating the trained and internally and externally validated model as a web-based tool or calculator is poised to offer an effective and easy approach towards the usage and acceptance of these models in the future daily practice. This approach has received significant attention in recent years. Thus, it is important that the usability and explainability of these models are measured to achieve such touted benefits. A usable and well-explained web-based tool further brings the use of these web-based tools closer to everyday clinical practices. Thus, the concept of more personalized and precision oncology can be achieved. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)

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16 pages, 5399 KiB

Open AccessArticle

Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years

by María Consuelo Sáiz-Manzanares, Raúl Marticorena-Sánchez and Álvar Arnaiz-González

Int. J. Environ. Res. Public Health 2022, 19(11), 6558; https://doi.org/10.3390/ijerph19116558 - 27 May 2022

Cited by 3 | Viewed by 1565

Abstract

Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0–6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users’ developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)

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Review

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26 pages, 4226 KiB

Open AccessReview

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

by Prabal Datta Barua, Jahmunah Vicnesh, Raj Gururajan, Shu Lih Oh, Elizabeth Palmer, Muhammad Mokhzaini Azizan, Nahrizul Adib Kadri and U. Rajendra Acharya

Int. J. Environ. Res. Public Health 2022, 19(3), 1192; https://doi.org/10.3390/ijerph19031192 - 21 Jan 2022

Cited by 35 | Viewed by 12974

Abstract

Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)

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Other

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5 pages, 400 KiB

Open AccessViewpoint

Doctors in Medical Data Sciences: A New Curriculum

by Sylvain Cussat-Blanc, Céline Castets-Renard and Paul Monsarrat

Int. J. Environ. Res. Public Health 2023, 20(1), 675; https://doi.org/10.3390/ijerph20010675 - 30 Dec 2022

Cited by 3 | Viewed by 1230

Abstract

Machine Learning (ML), a branch of Artificial Intelligence, which is competing with human experts in many specialized biomedical fields and will play an increasing role in precision medicine. As with any other technological advances in medicine, the keys to understanding must be integrated into practitioner training. To respond to this challenge, this viewpoint discusses some necessary changes in the health studies curriculum that could help practitioners to interpret decisions the made by a machine and question them in relation to the patient’s medical context. The complexity of technology and the inherent criticality of its use in medicine also necessitate a new medical profession. To achieve this objective, this viewpoint will propose new medical practitioners with skills in both medicine and data science: the Doctor in Medical Data Sciences. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)

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