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Diagnostics
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Artificial Intelligence in Clinical Decision Support—2nd Edition
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Artificial Intelligence in Clinical Decision Support—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2968

Special Issue Editor

Dr. Qingqing Mao

E-Mail Website
Guest Editor
Montera Inc., San Francisco, CA 94104, USA
Interests: artificial intelligence; machine learning; clinical decision support; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence has been increasingly used in Clinical Decision Support (CDS) systems to aid healthcare professionals in making timely and informed diagnoses and treatment decisions. The use of artificial intelligence has the potential to revolutionize CDS by providing more accurate and efficient diagnoses and treatment, improving patient outcomes, and reducing costs.

This Special Issue welcomes original research and review articles on developing and validating artificial intelligence-based clinical decision support algorithms and systems for chronic and acute conditions in various clinical settings. Potential topics include, but are not limited to, the following:

  • Predictive modeling using Electronic Health Record (EHR) data;
  • Real-time patient monitoring and risk prediction;
  • Diagnostic support using comprehensive medical records, including imaging and waveform data;
  • Treatment or therapy recommendation for chronic conditions;
  • Clinical trial design and optimization;
  • Personalized medicine.

Dr. Qingqing Mao
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. Diagnostics 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 2600 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
  • clinical decision support
  • predictive modeling
  • patient monitoring
  • diagnostic support
  • treatment recommendation
  • safety and privacy

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

Published Papers (3 papers)

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Research

10 pages, 1842 KiB  
Article
Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models?
by Reza Reiazi, Surendra Prajapati, Leonardo Che Fru, Dongyeon Lee and Mohammad Salehpour
Diagnostics 2025, 15(6), 786; https://doi.org/10.3390/diagnostics15060786 - 20 Mar 2025
Viewed by 298
Abstract
Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. [...] Read more.
Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility. Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency. Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment. Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support—2nd Edition)
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12 pages, 1380 KiB  
Article
Prediction of the Cause of Fundus-Obscuring Vitreous Hemorrhage Using Machine Learning
by Jinsoo Kim, Bo Sook Han, Joo Eun Ha, Min Seon Park, Soonil Kwon and Bum-Joo Cho
Diagnostics 2025, 15(3), 371; https://doi.org/10.3390/diagnostics15030371 - 4 Feb 2025
Viewed by 667
Abstract
Objectives: This study aimed to predict the unknown etiology of fundus-obscuring vitreous hemorrhage (FOVH) based on preoperative conditions using machine learning (ML) and to identify key preoperative factors. Methods: Medical records of 223 eyes from 204 patients who underwent vitrectomy for FOVH of [...] Read more.
Objectives: This study aimed to predict the unknown etiology of fundus-obscuring vitreous hemorrhage (FOVH) based on preoperative conditions using machine learning (ML) and to identify key preoperative factors. Methods: Medical records of 223 eyes from 204 patients who underwent vitrectomy for FOVH of unknown etiology between January 2012 and July 2022 were retrospectively reviewed. Preoperative data, including demographic information, systemic disease, ophthalmic history, and retinal status of the unaffected eye, were collected. The postoperatively identified etiologies of FOVH were categorized into six groups: proliferative diabetic retinopathy (PDR), retinal vein occlusion (RVO) or rupture of retinal arterial macroaneurysm, neovascular age-related macular degeneration (nAMD), retinal tear, Terson syndrome, and other causes. Four ML algorithms were trained and evaluated using seven-fold cross-validation. Results: The ML algorithms achieved mean accuracies of 76.2% for artificial neural network, 74.5% for XG-Boost, 74.4% for LASSO logistic regression, and 68.5% for decision tree. Key predictive factors commonly selected by the ML algorithms included PDR in the fellow eye, underlying diabetes mellitus, subarachnoid hemorrhage, and a history of retinal tear, RVO, or nAMD in the affected eye. Conclusions: The unknown etiology of FOVH could be predicted preoperatively with considerable accuracy by ML algorithms. Previous ophthalmic conditions in the affected eye and the condition of the fellow eye were important variables for prediction. This approach might assist in determining appropriate treatment plans. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support—2nd Edition)
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21 pages, 2149 KiB  
Article
Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes
by Robert P. Adelson, Anurag Garikipati, Yunfan Zhou, Madalina Ciobanu, Ken Tawara, Gina Barnes, Navan Preet Singh, Qingqing Mao and Ritankar Das
Diagnostics 2024, 14(11), 1152; https://doi.org/10.3390/diagnostics14111152 - 31 May 2024
Viewed by 1540
Abstract
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D [...] Read more.
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support—2nd Edition)
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