Editorial

9 pages, 480 KiB

Open AccessEditorial

Joint Expedition: Exploring the Intersection of Digital Health and AI in Precision Medicine with Team Integration

by Daniele Giansanti

J. Pers. Med. 2024, 14(4), 388; https://doi.org/10.3390/jpm14040388 - 04 Apr 2024

Viewed by 628

Precision medicine stands as a transformative force in the orbit of healthcare, fundamentally reshaping traditional approaches by customizing therapeutic interventions to align with the distinctive attributes of individual patients [...] Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

4 pages, 479 KiB

Open AccessEditorial

Precision Medicine 2.0: How Digital Health and AI Are Changing the Game

by Daniele Giansanti

J. Pers. Med. 2023, 13(7), 1057; https://doi.org/10.3390/jpm13071057 - 28 Jun 2023

Cited by 7 | Viewed by 2155

Abstract

In the era of rapid IT developments, the health domain is undergoing a considerable transformation [...] Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

Research

Jump to: Editorial, Review, Other

16 pages, 1809 KiB

Open AccessArticle

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

by Vincent W. S. Leung, Curtise K. C. Ng, Sai-Kit Lam, Po-Tsz Wong, Ka-Yan Ng, Cheuk-Hong Tam, Tsz-Ching Lee, Kin-Chun Chow, Yan-Kate Chow, Victor C. W. Tam, Shara W. Y. Lee, Fiona M. Y. Lim, Jackie Q. Wu and Jing Cai

J. Pers. Med. 2023, 13(12), 1643; https://doi.org/10.3390/jpm13121643 - 24 Nov 2023

Cited by 2 | Viewed by 1037

Abstract

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients [...] Read more.

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTV_prostate) on the pCT images; feature extraction from the CTV_prostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study’s results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

10 pages, 505 KiB

Open AccessArticle

Impact of the COVID-19 Pandemic on the Epidemiological Situation of Pulmonary Tuberculosis–Using Natural Language Processing

by Diego Morena, Carolina Campos, María Castillo, Miguel Alonso, María Benavent and José Luis Izquierdo

J. Pers. Med. 2023, 13(12), 1629; https://doi.org/10.3390/jpm13121629 - 22 Nov 2023

Cited by 1 | Viewed by 784

Abstract

Background: We aimed to analyze the impact of the COVID-19 pandemic on pulmonary tuberculosis (TB) using artificial intelligence. To do so, we compared the real-life situation during the pandemic with the pre-2020 situation. Methods: This non-interventional, retrospective, observational study applied natural language processing [...] Read more.

Background: We aimed to analyze the impact of the COVID-19 pandemic on pulmonary tuberculosis (TB) using artificial intelligence. To do so, we compared the real-life situation during the pandemic with the pre-2020 situation. Methods: This non-interventional, retrospective, observational study applied natural language processing to the electronic health records of the Castilla-La Mancha region of Spain. The analysis was conducted from January 2015 to December 2020. Results: A total of 2592 patients were diagnosed with pulmonary tuberculosis; 64.6% were males, and the mean age was 53.5 years (95%CI 53.0–54.0). In 2020, pulmonary tuberculosis diagnoses dropped by 28% compared to 2019. In total, 62 (14.2%) patients were diagnosed with COVID-19 and pulmonary tuberculosis coinfection in 2020, with a mean age of 52.3 years (95%CI 48.3–56.2). The main symptoms in these patients were dyspnea (27.4%) and cough (35.5%), although their comorbidities were no greater than patients with isolated TB. The female sex was more frequently affected, representing 53.4% of this patient subgroup. Conclusions: During the first year of the COVID-19 pandemic, a decrease was observed in the incidence of pulmonary tuberculosis. Women presented a significantly higher risk for pulmonary tuberculosis and COVID-19 coinfection, although the symptoms were not more severe than patients diagnosed with pulmonary tuberculosis alone. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

11 pages, 2032 KiB

Open AccessArticle

The Influence of Slice Thickness, Sharpness, and Contrast Adjustments on Inferior Alveolar Canal Segmentation on Cone-Beam Computed Tomography Scans: A Retrospective Study

by Julien Issa, Abanoub Riad, Raphael Olszewski and Marta Dyszkiewicz-Konwińska

J. Pers. Med. 2023, 13(10), 1518; https://doi.org/10.3390/jpm13101518 - 22 Oct 2023

Cited by 1 | Viewed by 988

Abstract

This retrospective study aims to investigate the impact of cone-beam computed tomography (CBCT) viewing parameters such as contrast, slice thickness, and sharpness on the identification of the inferior alveolar nerve (IAC). A total of 25 CBCT scans, resulting in 50 IACs, were assessed [...] Read more.

This retrospective study aims to investigate the impact of cone-beam computed tomography (CBCT) viewing parameters such as contrast, slice thickness, and sharpness on the identification of the inferior alveolar nerve (IAC). A total of 25 CBCT scans, resulting in 50 IACs, were assessed by two investigators using a three-score system (good, average, and poor) on cross-sectional images. Slice thicknesses of 0.25 mm, 0.5 mm, and 1 mm were tested, along with varying sharpness (0, 6, 8, and 10) and contrast (0, 400, 800, and 1200) settings. The results were statistically analyzed to determine the optimal slice thickness for improved visibility of IAC, followed by evaluating the influence of sharpness and contrast using the optimal thickness. The identified parameters were then validated by performing semi-automated segmentation of the IACs and structure overlapping to evaluate the mean distance. Inter-rater and intra-rater reliability were assessed using Kappa statistics, and inferential statistics used Pearson’s Chi-square test. Inter-rater and intra-rater reliability for all parameters were significant, ranging from 69% to 83%. A slice thickness of 0.25 mm showed consistently “good” visibility (80%). Sharpness values of zero and contrast values of 1200 also demonstrated high frequencies of “good” visibility. Overlap analysis resulted in an average mean distance of 0.295 mm and a standard deviation of 0.307 mm across all patients’ sides. The study revealed that a slice thickness of 0.25 mm, zero sharpness value, and higher contrast value of 1200 improved the visibility and accuracy of IAC segmentation in CBCT scans. The individual patient’s characteristics, such as anatomical variations, decreased bone density, and absence of canal walls cortication, should be considered when using these parameters. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

15 pages, 3274 KiB

Open AccessEditor’s ChoiceArticle

Challenging ChatGPT 3.5 in Senology—An Assessment of Concordance with Breast Cancer Tumor Board Decision Making

by Sebastian Griewing, Niklas Gremke, Uwe Wagner, Michael Lingenfelder, Sebastian Kuhn and Jelena Boekhoff

J. Pers. Med. 2023, 13(10), 1502; https://doi.org/10.3390/jpm13101502 - 16 Oct 2023

Cited by 7 | Viewed by 1676

Abstract

With the recent diffusion of access to publicly available large language models (LLMs), common interest in generative artificial-intelligence-based applications for medical purposes has skyrocketed. The increased use of these models by tech-savvy patients for personal health issues calls for a scientific evaluation of [...] Read more.

With the recent diffusion of access to publicly available large language models (LLMs), common interest in generative artificial-intelligence-based applications for medical purposes has skyrocketed. The increased use of these models by tech-savvy patients for personal health issues calls for a scientific evaluation of whether LLMs provide a satisfactory level of accuracy for treatment decisions. This observational study compares the concordance of treatment recommendations from the popular LLM ChatGPT 3.5 with those of a multidisciplinary tumor board for breast cancer (MTB). The study design builds on previous findings by combining an extended input model with patient profiles reflecting patho- and immunomorphological diversity of primary breast cancer, including primary metastasis and precancerous tumor stages. Overall concordance between the LLM and MTB is reached for half of the patient profiles, including precancerous lesions. In the assessment of invasive breast cancer profiles, the concordance amounts to 58.8%. Nevertheless, as the LLM makes considerably fraudulent decisions at times, we do not identify the current development status of publicly available LLMs to be adequate as a support tool for tumor boards. Gynecological oncologists should familiarize themselves with the capabilities of LLMs in order to understand and utilize their potential while keeping in mind potential risks and limitations. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

14 pages, 2853 KiB

Open AccessArticle

Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, BingChat, and Bard AI in Nephrology Literature Searches

by Noppawit Aiumtrakul, Charat Thongprayoon, Supawadee Suppadungsuk, Pajaree Krisanapan, Jing Miao, Fawad Qureshi and Wisit Cheungpasitporn

J. Pers. Med. 2023, 13(10), 1457; https://doi.org/10.3390/jpm13101457 - 30 Sep 2023

Cited by 10 | Viewed by 2520

Abstract

Background and Objectives: Literature reviews are foundational to understanding medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as potential aids in this domain, this study aimed to individually assess their citation accuracy within Nephrology, comparing their performance in [...] Read more.

Background and Objectives: Literature reviews are foundational to understanding medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as potential aids in this domain, this study aimed to individually assess their citation accuracy within Nephrology, comparing their performance in providing precise. Materials and Methods: We generated the prompt to solicit 20 references in Vancouver style in each 12 Nephrology topics, using ChatGPT, Bing Chat and Bard. We verified the existence and accuracy of the provided references using PubMed, Google Scholar, and Web of Science. We categorized the validity of the references from the AI chatbot into (1) incomplete, (2) fabricated, (3) inaccurate, and (4) accurate. Results: A total of 199 (83%), 158 (66%) and 112 (47%) unique references were provided from ChatGPT, Bing Chat and Bard, respectively. ChatGPT provided 76 (38%) accurate, 82 (41%) inaccurate, 32 (16%) fabricated and 9 (5%) incomplete references. Bing Chat provided 47 (30%) accurate, 77 (49%) inaccurate, 21 (13%) fabricated and 13 (8%) incomplete references. In contrast, Bard provided 3 (3%) accurate, 26 (23%) inaccurate, 71 (63%) fabricated and 12 (11%) incomplete references. The most common error type across platforms was incorrect DOIs. Conclusions: In the field of medicine, the necessity for faultless adherence to research integrity is highlighted, asserting that even small errors cannot be tolerated. The outcomes of this investigation draw attention to inconsistent citation accuracy across the different AI tools evaluated. Despite some promising results, the discrepancies identified call for a cautious and rigorous vetting of AI-sourced references in medicine. Such chatbots, before becoming standard tools, need substantial refinements to assure unwavering precision in their outputs. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Graphical abstract

24 pages, 13694 KiB

Open AccessArticle

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

by Luís B. Elvas, Miguel Nunes, Joao C. Ferreira, Miguel Sales Dias and Luís Brás Rosário

J. Pers. Med. 2023, 13(9), 1421; https://doi.org/10.3390/jpm13091421 - 21 Sep 2023

Cited by 1 | Viewed by 1388

Abstract

Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing [...] Read more.

Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

34 pages, 4757 KiB

Open AccessArticle

Intelligent Clinical Decision Support System for Managing COPD Patients

by José Pereira, Nuno Antunes, Joana Rosa, João C. Ferreira, Sandra Mogo and Manuel Pereira

J. Pers. Med. 2023, 13(9), 1359; https://doi.org/10.3390/jpm13091359 - 06 Sep 2023

Cited by 1 | Viewed by 1829

Abstract

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric [...] Read more.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients’ future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work’s CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research, Other

20 pages, 1342 KiB

Open AccessReview

Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

by Antonio Iannone and Daniele Giansanti

J. Pers. Med. 2024, 14(1), 41; https://doi.org/10.3390/jpm14010041 - 28 Dec 2023

Cited by 2 | Viewed by 1932

Abstract

(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) [...] Read more.

(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective of this study is to analyze the state of integration of AI with ATs in autism through a review. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 22 studies, including 7 reviews. (Key Content and Findings) The results reveal an early yet promising interest in integrating AI into autism assistive technologies. Exciting developments are currently underway at the intersection of AI and robotics, as well as in the creation of wearable automated devices like smart glasses. These innovations offer substantial potential for enhancing communication, interaction, and social engagement for individuals with autism. Presently, researchers are prioritizing innovation over establishing a solid presence within the healthcare domain, where issues such as regulation and acceptance demand increased attention. (Conclusions) As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

21 pages, 4145 KiB

Open AccessReview

Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT

by Jing Miao, Charat Thongprayoon, Supawadee Suppadungsuk, Oscar A. Garcia Valencia, Fawad Qureshi and Wisit Cheungpasitporn

J. Pers. Med. 2023, 13(12), 1681; https://doi.org/10.3390/jpm13121681 - 04 Dec 2023

Cited by 6 | Viewed by 2070

Abstract

The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed [...] Read more.

The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT’s performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT’s potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

31 pages, 3585 KiB

Open AccessReview

Personalized Care in Eye Health: Exploring Opportunities, Challenges, and the Road Ahead for Chatbots

by Mantapond Ittarat, Wisit Cheungpasitporn and Sunee Chansangpetch

J. Pers. Med. 2023, 13(12), 1679; https://doi.org/10.3390/jpm13121679 - 02 Dec 2023

Cited by 1 | Viewed by 1925

Abstract

In modern eye care, the adoption of ophthalmology chatbots stands out as a pivotal technological progression. These digital assistants present numerous benefits, such as better access to vital information, heightened patient interaction, and streamlined triaging. Recent evaluations have highlighted their performance in both [...] Read more.

In modern eye care, the adoption of ophthalmology chatbots stands out as a pivotal technological progression. These digital assistants present numerous benefits, such as better access to vital information, heightened patient interaction, and streamlined triaging. Recent evaluations have highlighted their performance in both the triage of ophthalmology conditions and ophthalmology knowledge assessment, underscoring their potential and areas for improvement. However, assimilating these chatbots into the prevailing healthcare infrastructures brings challenges. These encompass ethical dilemmas, legal compliance, seamless integration with electronic health records (EHR), and fostering effective dialogue with medical professionals. Addressing these challenges necessitates the creation of bespoke standards and protocols for ophthalmology chatbots. The horizon for these chatbots is illuminated by advancements and anticipated innovations, poised to redefine the delivery of eye care. The synergy of artificial intelligence (AI) and machine learning (ML) with chatbots amplifies their diagnostic prowess. Additionally, their capability to adapt linguistically and culturally ensures they can cater to a global patient demographic. In this article, we explore in detail the utilization of chatbots in ophthalmology, examining their accuracy, reliability, data protection, security, transparency, potential algorithmic biases, and ethical considerations. We provide a comprehensive review of their roles in the triage of ophthalmology conditions and knowledge assessment, emphasizing their significance and future potential in the field. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

17 pages, 1885 KiB

Open AccessReview

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

by Alaa Fawaz, Alessandra Ferraresi and Ciro Isidoro

J. Pers. Med. 2023, 13(11), 1590; https://doi.org/10.3390/jpm13111590 - 10 Nov 2023

Cited by 1 | Viewed by 1462

Abstract

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient’s life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of [...] Read more.

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient’s life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient’s response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient’s big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical–clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Graphical abstract

15 pages, 3347 KiB

Open AccessReview

Digital Twins in Healthcare: Methodological Challenges and Opportunities

by Charles Meijer, Hae-Won Uh and Said el Bouhaddani

J. Pers. Med. 2023, 13(10), 1522; https://doi.org/10.3390/jpm13101522 - 23 Oct 2023

Cited by 1 | Viewed by 2320

Abstract

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and [...] Read more.

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

Other

Jump to: Editorial, Research, Review

13 pages, 850 KiB

Open AccessSystematic Review

The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review

by Ben Allen

J. Pers. Med. 2024, 14(3), 277; https://doi.org/10.3390/jpm14030277 - 01 Mar 2024

Cited by 1 | Viewed by 1620

Abstract

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the [...] Read more.

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Graphical abstract

18 pages, 2556 KiB

Open AccessSystematic Review

The Use of Artificial Intelligence Algorithms in the Prognosis and Detection of Lymph Node Involvement in Head and Neck Cancer and Possible Impact in the Development of Personalized Therapeutic Strategy: A Systematic Review

by Luca Michelutti, Alessandro Tel, Marco Zeppieri, Tamara Ius, Salvatore Sembronio and Massimo Robiony

J. Pers. Med. 2023, 13(12), 1626; https://doi.org/10.3390/jpm13121626 - 21 Nov 2023

Cited by 1 | Viewed by 1147

Abstract

Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation [...] Read more.

Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation of patients with head and neck malignancies. The objective of this paper is to examine the currently available literature in the field of artificial intelligence applied to head and neck oncology, particularly in the prognostic evaluation of the patient with this kind of tumor, by means of a systematic review. The paper exposes an overview of the applications of artificial intelligence in deriving prognostic information related to the prediction of survival and recurrence and how these data may have a potential impact on the choice of therapeutic strategy, making it increasingly personalized. This systematic review was written following the PRISMA 2020 guidelines. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Graphical abstract

27 pages, 11524 KiB

Open AccessPerspective

Anterior Open Bite Malocclusion: From Clinical Treatment Strategies towards the Dissection of the Genetic Bases of the Disease Using Human and Collaborative Cross Mice Cohorts

by Iqbal M. Lone, Osayd Zohud, Kareem Midlej, Eva Paddenberg, Sebastian Krohn, Christian Kirschneck, Peter Proff, Nezar Watted and Fuad A. Iraqi

J. Pers. Med. 2023, 13(11), 1617; https://doi.org/10.3390/jpm13111617 - 17 Nov 2023

Cited by 1 | Viewed by 2697

Abstract

Anterior open bite malocclusion is a complex dental condition characterized by a lack of contact or overlap between the upper and lower front teeth. It can lead to difficulties with speech, chewing, and biting. Its etiology is multifactorial, involving a combination of genetic, [...] Read more.

Anterior open bite malocclusion is a complex dental condition characterized by a lack of contact or overlap between the upper and lower front teeth. It can lead to difficulties with speech, chewing, and biting. Its etiology is multifactorial, involving a combination of genetic, environmental, and developmental factors. Genetic studies have identified specific genes and signaling pathways involved in jaw growth, tooth eruption, and dental occlusion that may contribute to open bite development. Understanding the genetic and epigenetic factors contributing to skeletal open bite is crucial for developing effective prevention and treatment strategies. A thorough manual search was undertaken along with searches on PubMed, Scopus, Science Direct, and Web of Science for relevant studies published before June 2022. RCTs (clinical trials) and subsequent observational studies comprised the included studies. Orthodontic treatment is the primary approach for managing open bites, often involving braces, clear aligners, or other orthodontic appliances. In addition to orthodontic interventions, adjuvant therapies such as speech therapy and/or physiotherapy may be necessary. In some cases, surgical interventions may be necessary to correct underlying skeletal issues. Advancements in technology, such as 3D printing and computer-assisted design and manufacturing, have improved treatment precision and efficiency. Genetic research using animal models, such as the Collaborative Cross mouse population, offers insights into the genetic components of open bite and potential therapeutic targets. Identifying the underlying genetic factors and understanding their mechanisms can lead to the development of more precise treatments and preventive strategies for open bite. Here, we propose to perform human research using mouse models to generate debatable results. We anticipate that a genome-wide association study (GWAS) search for significant genes and their modifiers, an epigenetics-wide association study (EWAS), RNA-seq analysis, the integration of GWAS and expression-quantitative trait loci (eQTL), and micro-, small-, and long noncoding RNA analysis in tissues associated with open bite in humans and mice will uncover novel genes and genetic factors influencing this phenotype. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

9 pages, 457 KiB

Open AccessPerspective

The Next Frontier in Sarcoma Care: Digital Health, AI, and the Quest for Precision Medicine

by Bruno Fuchs, Gabriela Studer, Beata Bode-Lesniewska, Philip Heesen and on behalf of the Swiss Sarcoma Network

J. Pers. Med. 2023, 13(11), 1530; https://doi.org/10.3390/jpm13111530 - 25 Oct 2023

Cited by 3 | Viewed by 953

Abstract

The landscape of sarcoma care is on the cusp of a transformative era, spurred by the convergence of digital health and artificial intelligence (AI). This perspectives article explores the multifaceted opportunities and challenges in leveraging these technologies for value-based, precision sarcoma care. We [...] Read more.

The landscape of sarcoma care is on the cusp of a transformative era, spurred by the convergence of digital health and artificial intelligence (AI). This perspectives article explores the multifaceted opportunities and challenges in leveraging these technologies for value-based, precision sarcoma care. We delineate the current state-of-the-art methodologies and technologies in sarcoma care and outline their practical implications for healthcare providers, administrators, and policymakers. The article also addresses the limitations of AI and digital health platforms, emphasizing the need for high-quality data and ethical considerations. We delineate the promise held by the synergy of digital health platforms and AI algorithms in enhancing data-driven decision-making, outcome analytics, and personalized treatment planning. The concept of a sarcoma digital twin serves as an illustrative paradigm for this integration, offering a comprehensive, patient-centric view of the healthcare journey. The paper concludes with proposals for future research aimed at advancing the field, including the need for randomized controlled trials or target trial emulations and studies focusing on ethical and economic aspects. While the road to this transformative care is laden with ethical, regulatory, and practical challenges, we believe that the potential benefits far outweigh the obstacles. We conclude with a call to action for multidisciplinary collaboration and systemic adoption of these technologies, underscoring the urgency to act now for the future betterment of sarcoma care and healthcare at large. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

19 pages, 12038 KiB

Open AccessPerspective

Comprehensive Deciphering the Complexity of the Deep Bite: Insight from Animal Model to Human Subjects

by Nezar Watted, Iqbal M. Lone, Osayd Zohud, Kareem Midlej, Peter Proff and Fuad A. Iraqi

J. Pers. Med. 2023, 13(10), 1472; https://doi.org/10.3390/jpm13101472 - 08 Oct 2023

Cited by 5 | Viewed by 2200

Abstract

Deep bite is a malocclusion phenotype, defined as the misalignment in the vertical dimension of teeth and jaws and characterized by excessive overlap of the upper front teeth over the lower front teeth. Numerous factors, including genetics, environmental factors, and behavioral ones, might [...] Read more.

Deep bite is a malocclusion phenotype, defined as the misalignment in the vertical dimension of teeth and jaws and characterized by excessive overlap of the upper front teeth over the lower front teeth. Numerous factors, including genetics, environmental factors, and behavioral ones, might contribute to deep bite. In this study, we discuss the current clinical treatment strategies for deep bite, summarize the already published findings of genetic analysis associated with this complex phenotype, and their constraints. Finally, we propose a comprehensive roadmap to facilitate investigations for determining the genetic bases of this complex phenotype development. Initially, human deep bite phenotype, genetics of human deep bite, the prevalence of human deep bite, diagnosis, and treatment of human deep bite were the search terms for published publications. Here, we discuss these findings and their limitations and our view on future strategies for studying the genetic bases of this complex phenotype. New preventative and treatment methods for this widespread dental issue can be developed with the help of an understanding of the genetic and epigenetic variables that influence malocclusion. Additionally, malocclusion treatment may benefit from technological developments like 3D printing and computer-aided design and manufacture (CAD/CAM). These technologies enable the development of personalized surgical and orthodontic guidelines, enhancing the accuracy and effectiveness of treatment. Overall, the most significant results for the patient can only be achieved with a customized treatment plan created by an experienced orthodontic professional. To design a plan that meets the patient’s specific requirements and expectations, open communication between the patient and the orthodontist is essential. Here, we propose to conduct a genome-wide association study (GWAS), RNAseq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro and small RNA, and long noncoding RNA analysis in tissues associated with deep bite malocclusion in human, and complement it by the same approaches in the collaborative cross (CC) mouse model which offer a novel platform for identifying genetic factors as a cause of deep bite in mice, and subsequently can then be translated to humans. An additional direct outcome of this study is discovering novel genetic elements to advance our knowledge of how this malocclusion phenotype develops and open the venue for early identification of patients carrying the susceptible genetic factors so that we can offer early prevention and treatment strategies, a step towards applying a personalized medicine approach. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

22 pages, 7633 KiB

Open AccessPerspective

Narrating the Genetic Landscape of Human Class I Occlusion: A Perspective-Infused Review

by Iqbal M. Lone, Osayd Zohud, Kareem Midlej, Obaida Awadi, Samir Masarwa, Sebastian Krohn, Christian Kirschneck, Peter Proff, Nezar Watted and Fuad A. Iraqi

J. Pers. Med. 2023, 13(10), 1465; https://doi.org/10.3390/jpm13101465 - 06 Oct 2023

Cited by 6 | Viewed by 1160

Abstract

This review examines a prevalent condition with multifaceted etiology encompassing genetic, environmental, and oral behavioral factors. It stands as a significant ailment impacting oral functionality, aesthetics, and quality of life. Longitudinal studies indicate that malocclusion in primary dentition may progress to permanent malocclusion. [...] Read more.

This review examines a prevalent condition with multifaceted etiology encompassing genetic, environmental, and oral behavioral factors. It stands as a significant ailment impacting oral functionality, aesthetics, and quality of life. Longitudinal studies indicate that malocclusion in primary dentition may progress to permanent malocclusion. Recognizing and managing malocclusion in primary dentition is gaining prominence. The World Health Organization ranks malocclusions as the third most widespread oral health issue globally. Angle’s classification system is widely used to categorize malocclusions, with Class I occlusion considered the norm. However, its prevalence varies across populations due to genetic and examination disparities. Genetic factors, including variants in genes like MSX1, PAX9, and AXIN2, have been associated with an increased risk of Class I occlusion. This review aims to provide a comprehensive overview of clinical strategies for managing Class I occlusion and consolidate genetic insights from both human and murine populations. Additionally, genomic relationships among craniofacial genes will be assessed in individuals with Class I occlusion, along with a murine model, shedding light on phenotype–genotype associations of clinical relevance. The prevalence of Class I occlusion, its impact, and treatment approaches will be discussed, emphasizing the importance of early intervention. Additionally, the role of RNA alterations in skeletal Class I occlusion will be explored, focusing on variations in expression or structure that influence craniofacial development. Mouse models will be highlighted as crucial tools for investigating mandible size and prognathism and conducting QTL analysis to gain deeper genetic insights. This review amalgamates cellular, molecular, and clinical trait data to unravel correlations between malocclusion and Class I phenotypes. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

► Show Figures

Figure 1

7 pages, 224 KiB

Open AccessOpinion

Redesigning Primary Care: The Emergence of Artificial-Intelligence-Driven Symptom Diagnostic Tools

by Christian J. Wiedermann, Angelika Mahlknecht, Giuliano Piccoliori and Adolf Engl

J. Pers. Med. 2023, 13(9), 1379; https://doi.org/10.3390/jpm13091379 - 15 Sep 2023

Cited by 3 | Viewed by 1549

Abstract

Modern healthcare is facing a juxtaposition of increasing patient demands owing to an aging population and a decreasing general practitioner workforce, leading to strained access to primary care. The coronavirus disease 2019 pandemic has emphasized the potential for alternative consultation methods, highlighting opportunities [...] Read more.

Modern healthcare is facing a juxtaposition of increasing patient demands owing to an aging population and a decreasing general practitioner workforce, leading to strained access to primary care. The coronavirus disease 2019 pandemic has emphasized the potential for alternative consultation methods, highlighting opportunities to minimize unnecessary care. This article discusses the role of artificial-intelligence-driven symptom checkers, particularly their efficiency, utility, and challenges in primary care. Based on a study conducted in Italian general practices, insights from both physicians and patients were gathered regarding this emergent technology, highlighting differences in perceived utility, user satisfaction, and potential challenges. While symptom checkers are seen as potential tools for addressing healthcare challenges, concerns regarding their accuracy and the potential for misdiagnosis persist. Patients generally viewed them positively, valuing their ease of use and the empowerment they provide in managing health. However, some general practitioners perceive these tools as challenges to their expertise. This article proposes that artificial-intelligence-based symptom checkers can optimize medical-history taking for the benefit of both general practitioners and patients, with potential enhancements in complex diagnostic tasks rather than routine diagnoses. It underscores the importance of carefully integrating digital innovations while preserving the essential human touch in healthcare. Symptom checkers offer promising solutions; ensuring their accuracy, reliability, and effective integration into primary care requires rigorous research, clinical guidance, and an understanding of varied user perceptions. Collaboration among technologists, clinicians, and patients is paramount for the successful evolution of digital tools in healthcare. Full article

(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)

Journal Menu

Journal Browser

Transforming Precision Medicine: The Intersection of Digital Health and AI

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (21 papers)

Editorial

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI