Special Issue "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?"

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

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 20239

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Special Issue Editor

Dr. Daniele Giansanti
E-Mail Website
Guest Editor
1. Centro TISP, Istituto Superiore di Sanità, 00161 Roma, Italy
2. The Italian National Institute of Health, 299 Rome, Italy
Interests: robotics; artificial intelligence; neural networks; mHealth; digital health; rehabilitation; eHealth; smart technology; cybersecurity; informatics; big data; mental health; animal-assisted therapy; telemedicine; social robotics; acceptance; imaging; safety
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Special Issue Information

Dear Colleagues,

As a result of the incredible advances brought about by Information and Communication Technology (ICT) as seen today in electronic-health (e-health) and mobile-health (m-health), many new applications of both organ and cellular diagnostics are now possible. In the era of digitalization, we can speak specifically about the prospects of digital radiology and digital pathology.

Digital radiology includes the use of diagnostic imaging tools for organs based on systems compatible with Digital Imaging and Communications in Medicine (DICOM), known as DICOM-compliant. This includes not only instruments whose image formation processes are based on fields of interaction that use ionizing radiations, but also instruments based on ultrasound (ultrasound) and magnetic fields (nuclear magnetic resonance), for example.

Digital pathology, on the other hand, includes the use of digital processes related to instrumentation for cell diagnostics, which mainly takes two forms: histological and cytological. In this case, we speak of digital histology and digital cytology. However, other processes for digitizing information in the biomedical laboratory are also included in this area, such as those relating to the integration of cytometric reports.

Artificial intelligence (AI) is extending into the world of both digital radiology and digital-pathology and involves many scholars in the fields of technology and bioethics. These scholars are interested in both the potential applications of AI in feature recognition, diagnostics, automatic recognition, and quality control, and the limits and related problems. I invite you to contribute to this Special Issue, which has a broad scope. The following topics, though not exhaustive, will be considered: innovations in the field, including those correlated to the COVID-19 pandemic; the acceptance of AI in relation to all the actors involved, from the healthcare professionals to the patients.

This Special Issue of Healthcare welcomes commentaries, original research, short reports, and reviews on the challenges faced by health systems in this field.

Dr. Daniele Giansanti
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 monthly 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 1800 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

  • e-health
  • Medical devices
  • m-health
  • Digital-radiology
  • Digital-pathology
  • Picture Archive and Communication System
  • Artificial-intelligence
  • Electronic surveys

Published Papers (13 papers)

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Editorial

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Editorial
The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?
Healthcare 2021, 9(1), 30; https://doi.org/10.3390/healthcare9010030 - 31 Dec 2020
Cited by 8 | Viewed by 1501
Abstract
Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...] Full article

Research

Jump to: Editorial, Review, Other

Article
Medical Students’ Perceptions towards Digitization and Artificial Intelligence: A Mixed-Methods Study
Healthcare 2022, 10(4), 723; https://doi.org/10.3390/healthcare10040723 - 13 Apr 2022
Cited by 3 | Viewed by 1295
Abstract
Digital technologies in health care, including artificial intelligence (AI) and robotics, constantly increase. The aim of this study was to explore attitudes of 2020 medical students’ generation towards various aspects of eHealth technologies with the focus on AI using an exploratory sequential mixed-method [...] Read more.
Digital technologies in health care, including artificial intelligence (AI) and robotics, constantly increase. The aim of this study was to explore attitudes of 2020 medical students’ generation towards various aspects of eHealth technologies with the focus on AI using an exploratory sequential mixed-method analysis. Data from semi-structured interviews with 28 students from five medical faculties were used to construct an online survey send to about 80,000 medical students in Germany. Most students expressed positive attitudes towards digital applications in medicine. Students with a problem-based curriculum (PBC) in contrast to those with a science-based curriculum (SBC) and male undergraduate students think that AI solutions result in better diagnosis than those from physicians (p < 0.001). Male undergraduate students had the most positive view of AI (p < 0.002). Around 38% of the students felt ill-prepared and could not answer AI-related questions because digitization in medicine and AI are not a formal part of the medical curriculum. AI rating regarding the usefulness in diagnostics differed significantly between groups. Higher emphasis in medical curriculum of digital solutions in patient care is postulated. Full article
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Article
Role of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening
Healthcare 2022, 10(3), 468; https://doi.org/10.3390/healthcare10030468 - 03 Mar 2022
Cited by 2 | Viewed by 1588
Abstract
The accuracy of colposcopic diagnosis depends on the skill and proficiency of physicians. This study evaluated the feasibility of interpreting colposcopic images with the assistance of artificial intelligence (AI) for the diagnosis of high-grade cervical intraepithelial lesions. This study included female patients who [...] Read more.
The accuracy of colposcopic diagnosis depends on the skill and proficiency of physicians. This study evaluated the feasibility of interpreting colposcopic images with the assistance of artificial intelligence (AI) for the diagnosis of high-grade cervical intraepithelial lesions. This study included female patients who underwent colposcopy-guided biopsy in 2020 at two institutions in the Republic of Korea. Two experienced colposcopists reviewed all images separately. The Cerviray AI® system (AIDOT, Seoul, Korea) was used to interpret the cervical images. AI demonstrated improved sensitivity with comparable specificity and positive predictive value when compared with the colposcopic impressions of each clinician. The areas under the curve were greater with combined impressions (both AI and that of the two colposcopists) of high-grade lesions, when compared with the individual impressions of each colposcopist. This study highlights the feasibility of the application of an AI system in cervical cancer screening. AI interpretation can be utilized as an assisting tool in combination with human colposcopic evaluation of exocervix. Full article
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Article
The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders
Healthcare 2022, 10(1), 153; https://doi.org/10.3390/healthcare10010153 - 14 Jan 2022
Cited by 4 | Viewed by 944
Abstract
Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single [...] Read more.
Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives. Full article
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Article
Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis
Healthcare 2022, 10(1), 10; https://doi.org/10.3390/healthcare10010010 - 22 Dec 2021
Cited by 6 | Viewed by 2839
Abstract
Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious [...] Read more.
Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors’ keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field. Full article
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Article
Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders
Healthcare 2021, 9(10), 1347; https://doi.org/10.3390/healthcare9101347 - 11 Oct 2021
Cited by 4 | Viewed by 976
Abstract
Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An [...] Read more.
Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 73 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys. Full article
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Article
Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology
Healthcare 2021, 9(7), 834; https://doi.org/10.3390/healthcare9070834 - 01 Jul 2021
Cited by 6 | Viewed by 2307
Abstract
Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications [...] Read more.
Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges. Full article
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Review

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Review
The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus
Healthcare 2022, 10(3), 509; https://doi.org/10.3390/healthcare10030509 - 10 Mar 2022
Cited by 5 | Viewed by 1445
Abstract
Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and [...] Read more.
Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level. Full article
Review
Artificial Intelligence Advances in the World of Cardiovascular Imaging
Healthcare 2022, 10(1), 154; https://doi.org/10.3390/healthcare10010154 - 14 Jan 2022
Cited by 4 | Viewed by 1721
Abstract
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve [...] Read more.
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field. Full article
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Other

Reply
Reply to Giansanti, D. Comment on “Patel, B.; Makaryus, A.N. Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare 2022, 10, 154”
Healthcare 2022, 10(4), 735; https://doi.org/10.3390/healthcare10040735 - 15 Apr 2022
Cited by 1 | Viewed by 451
Abstract
Thank you for your interest and comment [...] Full article
Comment
Comment on Patel, B.; Makaryus, A.N. Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare 2022, 10, 154
Healthcare 2022, 10(4), 727; https://doi.org/10.3390/healthcare10040727 - 14 Apr 2022
Cited by 1 | Viewed by 536
Abstract
Regarding Dr. Makaryus’s interesting review study [...] Full article
Commentary
Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards
Healthcare 2021, 9(7), 858; https://doi.org/10.3390/healthcare9070858 - 07 Jul 2021
Cited by 6 | Viewed by 1879
Abstract
This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the [...] Read more.
This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the two fields of the digital cytology and the digital histology. Second, it illustrates the main fields in the employment of AI in DP. Third, it looks at the future directions of the research challenges from both a clinical and technological point of view. Fourth, it discusses the transversal problems among these challenges and implications and introduces the immediate work to implement. Full article
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Perspective
Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders
Healthcare 2021, 9(3), 331; https://doi.org/10.3390/healthcare9030331 - 15 Mar 2021
Cited by 5 | Viewed by 1256
Abstract
The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is [...] Read more.
The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is like a hammer in search of a nail. Notable developments and opportunities do not seem to be combined, now, in the time of the COVID-19 pandemic, with a stable, effective, and concrete use in clinical routine; the use of AI often seems limited to use in research applications. This study considers the future perceived integration of AI with digital radiology after the COVID-19 pandemic and proposes a methodology that, by means of a wide interaction of the involved actors, allows a positioning exercise for acceptance evaluation using a general purpose electronic survey. The methodology was tested on a first category of professionals, the medical radiology technicians (MRT), and allowed to (i) collect their impressions on the issue in a structured way, and (ii) collect their suggestions and their comments in order to create a specific tool for this professional figure to be used in scientific societies. This study is useful for the stakeholders in the field, and yielded several noteworthy observations, among them (iii) the perception of great development in thoracic radiography and CT, but a loss of opportunity in integration with non-radiological technologies; (iv) the belief that it is appropriate to invest in training and infrastructure dedicated to AI; and (v) the widespread idea that AI can become a strong complementary tool to human activity. From a general point of view, the study is a clear invitation to face the last yard of AI in digital radiology, a last yard that depends a lot on the opinion and the ability to accept these technologies by the operators of digital radiology. Full article
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