[go: up one dir, main page]
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advances in AI-Powered Medical Applications
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in AI-Powered Medical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 1586

Special Issue Editor

Dr. Pietro Piazzolla

E-Mail Website
Guest Editor
Dipartimento di Meccanica, Politecnico di Milano, 20133 Milano, Italy
Interests: 3D model; augmented reality computer graphics computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clinical practice is currently confronted with the possibility of adopting new technological tools that can drastically improve the effectiveness of health professionals in providing early diagnoses, and minimally invasive, patient-specific treatment options, as well as adding novel remote collaboration possibilities, or powerful educational potential.

Along with the widespread explosion of neural network applications, the ease of access to all kinds of sensors has drastically increased the amount of data that can be processed. Many new devices, relatively small in physical dimensions, are now capable of running AI-powered applications. All of this leads to the emergence of a vast field of studies that integrates medicine with different computer sciences and enabling technologies such as machine learning, neural networks, computer vision, robotics, sensors, mixed and augmented reality.

By focusing on different aspects of the clinical practice, ranging from surgical navigation systems to pre-operatory collaborative planning tools, from post-operatory pain management systems to diagnostic tools to analyze medical imagery, new systems are currently being investigated and implemented.

The aim of this Special Issue is to advance the scholarly understanding of how AI-powered medical applications can be used to further clinical practice, and specifically the 4P healthcare paradigm that requires it to be predictive, preventive, personalized, participatory.

In particular, the topics of interest for this Special Issue include but, are not limited to, the following:

  • New technological solutions for new AI-based applications in augmented medicine.
  • Virtual and/or augmented reality solutions for minimally invasive surgery.
  • Novel validation strategies for AI-based solutions in medicine.
  • Patient’s personal data protection strategies.
  • Ongoing education for medics in digital medicine strategies.
  • Smart surgery rooms.
  • Wearable system for collaborative diagnosis.
  • Digital twins.

Dr. Pietro Piazzolla
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. Applied Sciences 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 2400 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

  • augmented medicine
  • AI-powered medical technologies
  • digital medicine
  • clinical practice

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

8 pages, 761 KiB  
Article
Google Gemini’s Performance in Endodontics: A Study on Answer Precision and Reliability
by Victor Díaz-Flores García, Yolanda Freire, Marta Tortosa, Beatriz Tejedor, Roberto Estevez and Ana Suárez
Appl. Sci. 2024, 14(15), 6390; https://doi.org/10.3390/app14156390 - 23 Jul 2024
Viewed by 588
Abstract
(1) Background: Large language models (LLMs) are revolutionising various scientific fields by providing advanced support tools. However, the effectiveness of these applications depends on extensive, up-to-date databases to ensure certainty and predictive power. Transparency about information sources in Medicine remains a significant issue. [...] Read more.
(1) Background: Large language models (LLMs) are revolutionising various scientific fields by providing advanced support tools. However, the effectiveness of these applications depends on extensive, up-to-date databases to ensure certainty and predictive power. Transparency about information sources in Medicine remains a significant issue. (2) Methods: To evaluate Google Gemini’s accuracy and reproducibility in endodontic diagnosis and treatment, 60 questions were designed based on the European Society of Endodontology Position Statements. Thirty questions were randomly selected and answered using Gemini during April 2023. Two endodontic experts independently scored the answers using a 3-point Likert scale. Discrepancies were resolved by a third expert. The relative frequency and absolute percentage of responses were detailed. Accuracy was assessed using the Wald binomial method, and repeatability was assessed using percentage agreement, Brennan and Prediger’s coefficient, Conger’s generalised kappa, Fleiss’ kappa, Gwet’s AC, and Krippendorff’s alpha, all with 95% confidence intervals. Statistical analysis was performed using STATA software. (3) Results: A total of 900 answers were generated. The percentage of correct answers varied from 0% to 100% per question. Overall accuracy was 37.11% with a 95% confidence interval of 34.02–40.32%; (4) Conclusions: Gemini is not currently designed for medical use and therefore needs to be used with caution when considered for this purpose. Full article
(This article belongs to the Special Issue Advances in AI-Powered Medical Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Answer obtained to question 29 in all its repetitions.</p> Full article ">Figure 2
<p>Answer with a significant level of personalisation and familiarity.</p> Full article ">
11 pages, 1136 KiB  
Article
Chemoembolization for Hepatocellular Carcinoma Including Contrast Agent-Enhanced CT: Response Assessment Model on Radiomics and Artificial Intelligence
by Sungjin Yoon, Youngjae Kim, Juhyun Kim, Yunsoo Kim, Ohsang Kwon, Seungkak Shin, Jisoo Jeon and Seungjoon Choi
Appl. Sci. 2024, 14(9), 3613; https://doi.org/10.3390/app14093613 - 24 Apr 2024
Viewed by 654
Abstract
Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with [...] Read more.
Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were prospectively enrolled in this study as test data. A total of 100 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were retrospectively acquired for training data. Radiomic features were extracted from contrast-enhanced, liver computed tomography (CT) scans obtained before and after TACE. An AI algorithm was trained using the retrospective data and validated using the prospective test data to assess treatment outcomes. Results: This study evaluated 107 radiomic features and 5 clinical characteristics as potential predictors of progression-free survival and overall survival. The C-index was 0.582 as the graph of the cumulative hazard function, predicted by the variable configuration by using 112 radiomics features. The time-dependent AUROC was 0.6 ± 0.06 (mean ± SD). Among the selected radiomics features and clinical characteristics, baseline_glszm_SizeZoneNonUniformity, baseline_ glszm_ZoneVariance and tumor size had excellent performance as predictors of HCC response to TACE with AUROC of 0.853, 0.814 and 0.827, respectively. Conclusions: A radiomics-based AI model is capable of evaluating treatment outcomes for HCC treated with TACE. Full article
(This article belongs to the Special Issue Advances in AI-Powered Medical Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of the study population selection.</p> Full article ">Figure 2
<p>Graph of the cumulative hazard function predicted by the variable configuration for each test case. The colored lines are the 107 radiomics features and 5 clinical characteristics analyzed as potential predictors of overall survival, represented by the scikit-survival framework model. C-index scores were calculated to measure the performance of the model.</p> Full article ">Figure 3
<p>Graph of the time-dependent AUROC predicted by the variable configuration for each test case.</p> Full article ">Figure 4
<p>Result of feature selection.</p> Full article ">Figure 5
<p>Summary of the diagnostic performance of the radiomics features and clinical factors for the prognosis of HCC response to TACE treatment.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop