Artificial Intelligence Applications in Medicine: Second Edition

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 4268

Special Issue Editor


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Guest Editor
Department of Pathology, Faculty of Medicine, Tokai University School of Medicine, 143 Shimokasuya, Isehara 259-1193, Japan
Interests: artificial intelligence; molecular histopathology; pathology; neoplasia; inflammatory diseases; biomarkers; immune checkpoint; immuno-oncology; health care informatics; diagnosis and treatment
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Special Issue Information

Dear Colleagues,

I am glad to offer a second edition of our successful Special Issue "Artificial Intelligence Applications in Medicine".

This Special Issue aims to publish theoretical and empirical research in the interdisciplinary area of medicine and healthcare, with a special focus on artificial intelligence applications.

This includes health informatics research on disease prevention, early diagnosis, diagnosis, and treatment.

The birth of artificial intelligence (AI) was denoted by Alan Turing’s seminal work “Computing Machinery and Intelligence” (1), which described AI as systems that act like humans. AI is the engineering of intelligent computer programs (2), and combines computer science and robust datasets to solve problems (3). Using both machine learning and deep learning, it is possible to make predictions and classifications based on input data.

AI, machine learning, deep learning, and neural networks are terms that tend to be used interchangeably, but they have different meanings. Machine learning is a subfield of AI, deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. The depth refers to the number of node layers of a neural network that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

An artificial neural network has four main components: inputs, weights, a bias or threshold, and an output.

There is a difference of approaches between machine learning and deep learning. Machine learning tends to require structured data and uses traditional algorithms like linear regression. Deep learning employs neural networks and can handle large volumes of unstructured data.

AI in medicine uses machine learning and neural network models to search for medical data and discover observations to help improve health outcomes and patient experiences. Due to the recent advances in computer science and informatics, AI is quickly becoming a fundamental part of present-day healthcare.

There are several AI applications in medicine:

  1. Disease detection and diagnosis.
  2. Personalized disease treatment.
  3. Medical imaging.
  4. Clinical trial efficiency.
  5. Accelerated drug development.

This Special Issue welcomes research on the application of AI in medicine.

Dr. Joaquim Carreras
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • artificial neural networks
  • prognosis
  • treatment
  • medicine
  • health care
  • pathology

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Published Papers (2 papers)

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Research

19 pages, 19820 KiB  
Article
Changes in MRI Workflow of Multiple Sclerosis after Introduction of an AI-Software: A Qualitative Study
by Eiko Rathmann, Pia Hemkemeier, Susan Raths, Matthias Grothe, Fiona Mankertz, Norbert Hosten and Steffen Flessa
Healthcare 2024, 12(10), 978; https://doi.org/10.3390/healthcare12100978 - 9 May 2024
Viewed by 1214
Abstract
The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis [...] Read more.
The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine: Second Edition)
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14 pages, 3228 KiB  
Article
Gynaecological Artificial Intelligence Diagnostics (GAID) GAID and Its Performance as a Tool for the Specialist Doctor
by Panayiotis Tanos, Ioannis Yiangou, Giorgos Prokopiou, Antonis Kakas and Vasilios Tanos
Healthcare 2024, 12(2), 223; https://doi.org/10.3390/healthcare12020223 - 16 Jan 2024
Viewed by 2391
Abstract
Background: Human-centric artificial intelligence (HCAI) aims to provide support systems that can act as peer companions to an expert in a specific domain, by simulating their way of thinking and decision-making in solving real-life problems. The gynaecological artificial intelligence diagnostics (GAID) assistant is [...] Read more.
Background: Human-centric artificial intelligence (HCAI) aims to provide support systems that can act as peer companions to an expert in a specific domain, by simulating their way of thinking and decision-making in solving real-life problems. The gynaecological artificial intelligence diagnostics (GAID) assistant is such a system. Based on artificial intelligence (AI) argumentation technology, it was developed to incorporate, as much as possible, a complete representation of the medical knowledge in gynaecology and to become a real-life tool that will practically enhance the quality of healthcare services and reduce stress for the clinician. Our study aimed to evaluate GAIDS’ efficacy and accuracy in assisting the working expert gynaecologist during day-to-day clinical practice. Methods: Knowledge-based systems utilize a knowledge base (theory) which holds evidence-based rules (“IF-THEN” statements) that are used to prove whether a conclusion (such as a disease, medication or treatment) is possible or not, given a set of input data. This approach uses argumentation frameworks, where rules act as claims that support a specific decision (arguments) and argue for its dominance over others. The result is a set of admissible arguments which support the final decision and explain its cause. Results: Based on seven different subcategories of gynaecological presentations—bleeding, endocrinology, cancer, pelvic pain, urogynaecology, sexually transmitted infections and vulva pathology in fifty patients—GAID demonstrates an average overall closeness accuracy of zero point eighty-seven. Since the system provides explanations for supporting a diagnosis against other possible diseases, this evaluation process further allowed for a learning process of modular improvement in the system of the diagnostic discrepancies between the system and the specialist. Conclusions: GAID successfully demonstrates an average accuracy of zero point eighty-seven when measuring the closeness of the system’s diagnosis to that of the senior consultant. The system further provides meaningful and helpful explanations for its diagnoses that can help clinicians to develop an increasing level of trust towards the system. It also provides a practical database, which can be used as a structured history-taking assistant and a friendly, patient record-keeper, while improving precision by providing a full list of differential diagnoses. Importantly, the design and implementation of the system facilitates its continuous development with a set methodology that allows minimal revision of the system in the face of new information. Further large-scale studies are required to evaluate GAID more thoroughly and to identify its limiting boundaries. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine: Second Edition)
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