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Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 5519

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Special Issue Editor

Prof. Dr. Simon W. Rabkin

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Guest Editor

Department of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: heart failure; hypertension; lipids; aortic aneurysm; cardiovascular disease and its therapy; translational medicine; AI in cardiovascular diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has increasingly been applied in medical research. The field of medicine is data-rich, and machine learning, which utilizes computer systems to make decisions based on data, can provide invaluable insights into these data. There are many ways in which machine learning can aid in the assessment, diagnosis, and treatment of diseases, from analyzing medical records or diagnostic testing to predicting patient outcomes.

This Special Issue titled "Machine Learning and Medicine: the interface of Medicine, Engineering and Artificial Intelligence" explores the potential applications and challenges of machine learning in the field of medicine. It highlights the growing interface between computer science and medicine, and how the integration of these fields can provide innovative solutions to healthcare challenges.

The Special Issue covers a diverse set of topics, including the use of machine learning in disease classification, diagnosis, treatment, and patient monitoring. It also delves into issues related to the ethical implications of using machine learning in healthcare, such as data privacy and security.

Overall, the Special Issue highlights the enormous potential of machine learning techniques in transforming healthcare, while also acknowledging the limitations and challenges associated with their implementation. It provides an important platform for researchers and practitioners to share their expertise on this rapidly evolving field and encourage interdisciplinary collaboration to shape the future of medicine.

Prof. Dr. Simon Rabkin
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. Bioengineering 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 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

medical informatics
healthcare analytics
predictive modeling
digital health
precision medicine
machine learning
artificial intelligence

Published Papers (5 papers)

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Research

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15 pages, 1943 KiB

Open AccessArticle

Improving Radiology Report Generation Quality and Diversity through Reinforcement Learning and Text Augmentation

by Daniel Parres, Alberto Albiol and Roberto Paredes

Bioengineering 2024, 11(4), 351; https://doi.org/10.3390/bioengineering11040351 - 03 Apr 2024

Viewed by 646

Abstract

Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder–decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text augmentation to tackle these issues, significantly improving report quality and variability. By employing RadGraph as a reward metric and innovating in text augmentation, we surpass existing benchmarks like BLEU4, ROUGE-L, F₁CheXbert, and RadGraph, setting new standards for report accuracy and diversity on MIMIC-CXR and Open-i datasets. Our VED model achieves F₁-scores of

66.2

for CheXbert and

37.8

for RadGraph on the MIMIC-CXR dataset, and

54.7

and

45.6

, respectively, on Open-i. These outcomes represent a significant breakthrough in the RRG field. The findings and implementation of the proposed approach, aimed at enhancing diagnostic precision and radiological interpretations in clinical settings, are publicly available on GitHub to encourage further advancements in the field. Full article

(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)

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9 pages, 571 KiB

Open AccessArticle

Exploring Diagnostic Precision and Triage Proficiency: A Comparative Study of GPT-4 and Bard in Addressing Common Ophthalmic Complaints

by Roya Zandi, Joseph D. Fahey, Michael Drakopoulos, John M. Bryan, Siyuan Dong, Paul J. Bryar, Ann E. Bidwell, R. Chris Bowen, Jeremy A. Lavine and Rukhsana G. Mirza

Bioengineering 2024, 11(2), 120; https://doi.org/10.3390/bioengineering11020120 - 26 Jan 2024

Cited by 1 | Viewed by 1226

Abstract

In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis and triage. In this in silico study, 80 simulated patient complaints in ophthalmology with varying urgency levels and clinical descriptors were entered into both ChatGPT and Bard in a systematic 3-step submission process asking chatbots to triage, diagnose, and evaluate urgency. Three ophthalmologists graded chatbot responses. Chatbots were significantly better at ophthalmic triage than diagnosis (90.0% appropriate triage vs. 48.8% correct leading diagnosis; p < 0.001), and GPT-4 performed better than Bard for appropriate triage recommendations (96.3% vs. 83.8%; p = 0.008), grader satisfaction for patient use (81.3% vs. 55.0%; p < 0.001), and lower potential harm rates (6.3% vs. 20.0%; p = 0.010). More descriptors improved the accuracy of diagnosis for both GPT-4 and Bard. These results indicate that chatbots may not need to recognize the correct diagnosis to provide appropriate ophthalmic triage, and there is a potential utility of these tools in aiding patients or triage staff; however, they are not a replacement for professional ophthalmic evaluation or advice. Full article

(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)

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16 pages, 4681 KiB

Open AccessArticle

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

by Iciar Usategui, Yoel Arroyo, Ana María Torres, Julia Barbado and Jorge Mateo

Bioengineering 2024, 11(1), 90; https://doi.org/10.3390/bioengineering11010090 - 17 Jan 2024

Cited by 2 | Viewed by 1172

Abstract

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems. Full article

(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)

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26 pages, 3577 KiB

Open AccessArticle

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques

by Erum Yousef Abbasi, Zhongliang Deng, Arif Hussain Magsi, Qasim Ali, Kamlesh Kumar and Asma Zubedi

Bioengineering 2024, 11(1), 43; https://doi.org/10.3390/bioengineering11010043 - 31 Dec 2023

Viewed by 1251

Abstract

The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan–Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy. Full article

(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)

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Review

Jump to: Research

17 pages, 1824 KiB

Open AccessReview

Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

by Carlo Metta, Andrea Beretta, Roberto Pellungrini, Salvatore Rinzivillo and Fosca Giannotti

Bioengineering 2024, 11(4), 369; https://doi.org/10.3390/bioengineering11040369 - 12 Apr 2024

Viewed by 532

Abstract

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards. Full article

(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)

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