Objectives: We aimed to describe the cardiopulmonary function during exercise
and the health-related quality of life (HRQoL) in patients with a history of COVID-19
pneumonia, stratified by.....
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Objectives: We aimed to describe the cardiopulmonary function during exercise
and the health-related quality of life (HRQoL) in patients with a history of COVID-19
pneumonia, stratified by chest computed tomography (CT) findings at baseline. Methods: Among 77 consecutive patients with COVID-19 who were discharged from the
Pulmonology Ward between March 2020 and April 2021, 28 (mean age 54.3 ± 8.6 years,
8 females) agreed to participate to the current study. The participants were analyzed in two
groups based on pulmonary involvement (PI) at baseline chest CT applying a threshold of
25%. A consequent artificial intelligence (AI)-guided total opacity score (TOS) was calculated in a subgroup of 22 patients. A cardiopulmonary exercise test (CPET) was conducted
on average 8.4 (±1.9) months after discharge from the hospital. HRQoL was defined using
the short-form (SF-36) questionnaire. The primary outcome was exercise intolerance that
was defined as a peak oxygen uptake (V′O2peak) < 80% predicted. Secondary outcomes
were ventilatory limitation, defined as breathing reserve < 15%, circulatory limitation,
defined as oxygen pulse predicted below 80%, and deconditioning, defined as exercise
intolerance in the absence of ventilatory and circulatory limitations. Other secondary outcomes included the SF-36 domains. Results: In all, 15 patients had at least 25% PI (53.6%)
at baseline chest CT. Exercise intolerance was observed in ten patients (35.7%), six due
to deconditioning and four due to circulatory limitation; none had ventilatory limitation.
AI-guided TOS was 30.1 ± 24.4% vs. 6.1 ± 4.8% (p < 0.001) at baseline, and 1.7 ± 3.0% vs.
0.2 ± 0.7% (nonsignificant) at follow-up in high and low PI groups, respectively. The physical functioning (PF) domain score of the SF-36 questionnaire was 66.3 ± 19.4 vs. 85.0 ± 13.1
in high and low PI groups, respectively (p = 0.007). Other SF-36 domains did not differ
significantly between the groups. A high PI at baseline was inversely correlated with
V′O
2peak (standardized β coefficient = −0.436; 95% CI −26.1; −0.7; p = 0.040) and with PF
scores (standardized β coefficient −0.654; 95% CI −41.3; −7.6; p = 0.006) adjusted for age,
sex, body mass index and lung diffusion capacity. Conclusions: One-third of participants
experienced exercise intolerance eight months after COVID-19 pneumonia. A higher PI at
baseline was significantly associated with exercise intolerance and PF. Notwithstanding, the radiological PI was resolved, and the exercise intolerance was mainly explained not by
ventilatory limitation but by circulatory limitation and deconditioning.
Keywords: COVID-19; radiological pulmonary involvement; exercise intolerance; health-related
quality of life
Authors: Kholoud Elnaggar, Mostafa M. El-Gayar and Mohammed Elmogy
Abstract:
Background: Mental disorders are disturbances of brain functions that cause
cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees.
One of these disorders is.....
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Authors: Kholoud Elnaggar, Mostafa M. El-Gayar and Mohammed Elmogy
Background: Mental disorders are disturbances of brain functions that cause
cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees.
One of these disorders is depression, a significant factor contributing to the increase in
suicide cases worldwide. Consequently, depression has become a significant public health
issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms
underlying mental disorders and enhancing the understanding of MDD. Methods: This
survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine
learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements
in EEG preprocessing, feature extraction, and model development, showcasing how these
approaches enhance the diagnostic precision, scalability, and automation of depression
detection. Results: This survey is distinguished from prior reviews by addressing their
limitations and providing researchers with valuable insights for future studies. It offers a
comprehensive comparison of ML and DL approaches utilizing EEG and an overview of
the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future
directions and challenges, such as enhancing diagnostic robustness with data augmentation
techniques and optimizing EEG channel selection for improved accuracy. The potential of
transfer learning and encoder-decoder architectures to leverage pre-trained models and
enhance diagnostic performance is also discussed. Advancements in feature extraction
methods for automated depression diagnosis are highlighted as avenues for improving
ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices
with EEG for continuous mental health monitoring and distinguishing between different
types of depression are identified as critical research areas. Finally, the review emphasizes
improving the reliability and predictability of computational intelligence-based models to
advance depression diagnosis. Conclusions: This study will serve as a well-organized and
helpful reference for researchers working on detecting depression using EEG signals and
provide insights into the future directions outlined above, guiding further advancements
in the field.
Keywords: mild depression disorder (MDD) detection; EEG signal features and biomarkers;
optimizing electroencephalogram (EEG) channel selection; EEG preprocessing methods;
integrating IoT and EEG; ML and DL methods for depression diagnosis
Authors: Feiyu Chen, Linghui Sun, Boyu Jiang, Xu Huo, Xiuxiu Pan, Chun Feng and Zhirong Zhang
Abstract:
The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving
technological advancement and industrial upgrading in.....
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Authors: Feiyu Chen, Linghui Sun, Boyu Jiang, Xu Huo, Xiuxiu Pan, Chun Feng and Zhirong Zhang
The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving
technological advancement and industrial upgrading in this field. This paper systematically
reviews the current applications and development trends of AI in unconventional oil and
gas exploration and development, covering major research achievements in geological
exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced
oil recovery; and health, safety, and environment management. This paper reviews how
deep learning helps predict gas distribution and classify rock types. It also explains how
machine learning improves reservoir simulation and history matching. Additionally, we
discuss the use of LSTM and DNN models in production forecasting, showing how AI
has progressed from early experiments to fully integrated solutions. However, challenges
such as data quality, model generalization, and interpretability remain significant. Based
on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with
engineering mechanisms; and advancing interpretable modeling and transfer learning
techniques. With next-generation intelligent systems, AI will further improve efficiency
and sustainability in unconventional oil and gas development.
Keywords: artificial intelligence; unconventional reservoir; geological exploration; production
forecasting
Authors: Dong-Jin Kim, Yun-Su Lee, Eun-Raye Jeon and Kwang Joon Kim
Abstract:
South Korea is promoting digital healthcare services in the public sector. One notable
initiative is the “artificial intelligence and the internet of things (AI–IoT)-based healthcare project
for senior citizens”,.....
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Authors: Dong-Jin Kim, Yun-Su Lee, Eun-Raye Jeon and Kwang Joon Kim
South Korea is promoting digital healthcare services in the public sector. One notable
initiative is the “artificial intelligence and the internet of things (AI–IoT)-based healthcare project
for senior citizens”, which was implemented by the Korea Health Promotion Institute (KHPI). This
project utilized an IoT-based digital healthcare service that integrates information technology and
screen-based AI speaker functions. Services through this project are intended for senior citizens aged
65 years (or older) who face challenges in visiting public healthcare institutions owing to limitations
on outdoor activities, especially in the post-coronavirus 2019 era. This article shares the recent
outcomes of this project and outlines the mid-to-long-term development strategies for this style of
South Korean digital healthcare initiatives.
Keywords: digital healthcare; senior citizens; artificial intelligence; internet of things
Authors: Jieshu Wu, Linjing Dong, Yating Sun, Xianfeng Zhao, Junai Gan and Zhixu Wang
Abstract:
Breastfeeding is widely recognized as the gold standard for infant nutrition, benefitting
infants’ gastrointestinal tracts. Stool analysis helps in understanding pediatric gastrointestinal health,
but the effectiveness of automated fecal.....
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Authors: Jieshu Wu, Linjing Dong, Yating Sun, Xianfeng Zhao, Junai Gan and Zhixu Wang
Breastfeeding is widely recognized as the gold standard for infant nutrition, benefitting
infants’ gastrointestinal tracts. Stool analysis helps in understanding pediatric gastrointestinal health,
but the effectiveness of automated fecal consistency evaluation by parents of breastfeeding infants
has not been investigated. Photographs of one-month-old infants’ feces on diapers were taken
via a smartphone app and independently categorized by Artificial Intelligence (AI), parents, and
researchers. The accuracy of the evaluations of the AI and the parents was assessed and compared.
The factors contributing to assessment bias and app user characteristics were also explored. A total of
98 mother–infant pairs contributed 905 fecal images, 94.0% of which were identified as loose feces. AI
and standard scores agreed in 95.8% of cases, demonstrating good agreement (intraclass correlation
coefficient (ICC) = 0.782, Kendall’s coefficient of concordance W (Kendall’s W) = 0.840, Kendall’s
tau = 0.690), whereas only 66.9% of parental scores agreed with standard scores, demonstrating low
agreement (ICC = 0.070, Kendall’s W = 0.523, Kendall’s tau = 0.058). The more often a mother had one
or more of the following characteristics, unemployment, education level of junior college or below,
cesarean section, and risk for postpartum depression (PPD), the more her appraisal tended to be
inaccurate (p < 0.05). Each point increase in the Edinburgh Postnatal Depression Scale (EPDS) score
increased the deviation by 0.023 points (p < 0.05), which was significant only in employed or cesarean
section mothers (p < 0.05). An AI-based stool evaluation service has the potential to assist mothers in
assessing infant stool consistency by providing an accurate, automated, and objective assessment,
thereby helping to monitor and ensure the well-being of infants.
Authors: Samaneh Omranian, Alireza Khoddam, Celeste Campos-Castillo, Sajjad Fouladvand, Susan McRoy and Janet Rich-Edwards
Abstract:
We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine
hesitancy among healthcare providers by examining their open-text comments. We conducted a
longitudinal survey starting in Spring of.....
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Authors: Samaneh Omranian, Alireza Khoddam, Celeste Campos-Castillo, Sajjad Fouladvand, Susan McRoy and Janet Rich-Edwards
We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine
hesitancy among healthcare providers by examining their open-text comments. We conducted a
longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in
three national cohorts to assess how the pandemic has affected their livelihood. In January and
March–April 2021 surveys, participants were invited to contribute open-text comments and answer
specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified
vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-
19 vaccine. We collected 1970 comments from VH participants and trained two machine learning
(ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each
comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility)
related to adopting disease prevention activities. The second predictive model used the words in
January comments to predict the vaccine status of VH in March–April 2021; vaccine status was
correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers,
17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the
VH participants citing a barrier, such as allergic reactions and side effects, had the most associated
change in vaccine status from VH to later receiving a vaccine.
Keywords: COVID-19 vaccination; healthcare providers; Nurses’ Health Study; vaccine hesitancy;
health belief model; artificial intelligence; natural language processing; text classification
Purpose: This systematic review and meta-analysis aimed to investigate the effects of
artificial intelligence chatbot interventions on health outcomes in women. Methods: Ten relevant
studies published between 2019 and.....
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Purpose: This systematic review and meta-analysis aimed to investigate the effects of
artificial intelligence chatbot interventions on health outcomes in women. Methods: Ten relevant
studies published between 2019 and 2023 were extracted from the PubMed, Cochrane Library, EMBASE, CINAHL, and RISS databases in accordance with the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses guidelines. This review focused on experimental studies concerning
chatbot interventions in women’s health. The literature was assessed using the ROB 2 quality appraisal checklist, and the results were visualized with a risk-of-bias visualization program. Results:
This review encompassed seven randomized controlled trials and three single-group experimental
studies. Chatbots were effective in addressing anxiety, depression, distress, healthy relationships,
cancer self-care behavior, preconception intentions, risk perception in eating disorders, and gender
attitudes. Chatbot users experienced benefits in terms of internalization, acceptability, feasibility, and
interaction. A meta-analysis of three studies revealed significant effects in reducing anxiety (I2 = 0%,
Q = 8.10, p < 0.017), with an effect size of −0.30 (95% CI, −0.42 to −0.18). Conclusions: Artificial
intelligence chatbot interventions had positive effects on physical, physiological, and cognitive health
outcomes. Using chatbots may represent pivotal nursing interventions for female populations to
improve health status and support women socially as a form of digital therapy.
Keywords: anxiety; artificial intelligence; meta-analysis; systematic review; women’s health
Authors: Anne White, Mary Beth Maguire, Austin Brown and Diane Keen
Abstract:
As the global population ages, nurses with a positive attitude toward caring for older adults
is crucial. However, studies indicate that nursing students often exhibit negative attitudes toward
older.....
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Authors: Anne White, Mary Beth Maguire, Austin Brown and Diane Keen
As the global population ages, nurses with a positive attitude toward caring for older adults
is crucial. However, studies indicate that nursing students often exhibit negative attitudes toward
older adults. This study aimed to determine if a three-phased educational intervention significantly
improved nursing students’ attitudes toward older adults. A pre/post-test study design was used to
measure the change in nursing students’ attitudes toward older adults, as measured by the UCLA
Geriatrics Attitudes Survey, after participating in an Artificial Intelligence in Education learning
event (n = 151). Results indicate that post-intervention scores (M = 35.07, SD = 5.34) increased from
pre-intervention scores (M = 34.50, SD = 4.86). This difference was statistically significant at the
0.10 significance level (t = 1.88, p = 0.06). Incorporating artificial intelligence technology in a learning
event is an effective educational strategy due to its convenience, repetition, and measurable learning
outcomes. Improved attitudes toward older adults are foundational for delivering competent care to
a rapidly growing aging population. This study was prospectively registered with the university’s
Institutional Review Board (IRB) on 30 July 2021 with the registration number IRB-FY22-3.
Authors: Sara Jayousi, Chiara Barchielli, Marco Alaimo, Stefano Caputo, Marzia Paffetti, Paolo Zoppi and Lorenzo Mucchi
Abstract:
Over the past few decades, Information and Communication Technologies (ICT) have
revolutionized the fields of nursing and patient healthcare management. This scoping review and
the accompanying case studies shed.....
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Authors: Sara Jayousi, Chiara Barchielli, Marco Alaimo, Stefano Caputo, Marzia Paffetti, Paolo Zoppi and Lorenzo Mucchi
Over the past few decades, Information and Communication Technologies (ICT) have
revolutionized the fields of nursing and patient healthcare management. This scoping review and
the accompanying case studies shed light on the extensive scope and impact of ICT in these critical
healthcare domains. The scoping review explores the wide array of ICT tools employed in nursing
care and patient healthcare management. These tools encompass electronic health records systems,
mobile applications, telemedicine solutions, remote monitoring systems, and more. This article
underscores how these technologies have enhanced the efficiency, accuracy, and accessibility of
clinical information, contributing to improved patient care. ICT revolution has revitalized nursing
care and patient management, improving the quality of care and patient satisfaction. This review and
the accompanying case studies emphasize the ongoing potential of ICT in the healthcare sector and
call for further research to maximize its benefits.
Keywords: Informationand Communication Technologies; healthcare; nursing; patients’ management;
Internet of Things; Artificial Intelligence; health monitoring
Authors: Garry Brydges, Abhineet Uppal and Vijaya Gottumukkala
Abstract:
This narrative review explores the utilization of machine learning (ML) and artificial
intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to.....
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Authors: Garry Brydges, Abhineet Uppal and Vijaya Gottumukkala
This narrative review explores the utilization of machine learning (ML) and artificial
intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical
decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons,
critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery
throughout the perioperative continuum.
Authors: Pau Climent-Pérez, Agustín Ernesto Martínez-González, and Pedro Andreo-Martínez
Abstract:
Background: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder whose etiology is not known today, but everything indicates that it is multifactorial.
For example, genetic and.....
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Authors: Pau Climent-Pérez, Agustín Ernesto Martínez-González, and Pedro Andreo-Martínez
Background: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder whose etiology is not known today, but everything indicates that it is multifactorial.
For example, genetic and epigenetic factors seem to be involved in the etiology of ASD. In recent
years, there has been an increase in studies on the implications of gut microbiota (GM) on the behavior
of children with ASD given that dysbiosis in GM may trigger the onset, development and progression
of ASD through the microbiota–gut–brain axis. At the same time, significant progress has occurred in
the development of artificial intelligence (AI). Methods: The aim of the present study was to perform
a systematic review of articles using AI to analyze GM in individuals with ASD. In line with the
PRISMA model, 12 articles using AI to analyze GM in ASD were selected. Results: Outcomes reveal
that the majority of relevant studies on this topic have been conducted in China (33.3%) and Italy
(25%), followed by the Netherlands (16.6%), Mexico (16.6%) and South Korea (8.3%). Conclusions:
The bacteria Bifidobacterium is the most relevant biomarker with regard to ASD. Although AI provides
a very promising approach to data analysis, caution is needed to avoid the over-interpretation of
preliminary findings. A first step must be taken to analyze GM in a representative general population
and ASD samples in order to obtain a GM standard according to age, sex and country. Thus, more
work is required to bridge the gap between AI in mental health research and clinical care in ASD.
Keywords: artificial intelligence; autism spectrum disorders; gut microbiota; machine learning
This paper delves into the fusion of artificial intelligence (AI) and emotional intelligence
(EQ) by analyzing the frameworks of international sustainability agendas driven by UNESCO, WEF,
and UNICEF. It.....
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This paper delves into the fusion of artificial intelligence (AI) and emotional intelligence
(EQ) by analyzing the frameworks of international sustainability agendas driven by UNESCO, WEF,
and UNICEF. It explores the potential of AI integrated with EQ to effectively address the Sustainable
Development Goals (SDGs), with a focus on education, healthcare, and environmental sustainability.
The integration of EQ into AI use is pivotal in using AI to improve educational outcomes and health
services, as emphasized by UNESCO and UNICEF’s significant initiatives. This paper highlights the
evolving role of AI in understanding and managing human emotions, particularly in personalizing
education and healthcare. It proposes that the ethical use of AI, combined with EQ principles,
has the power to transform societal interactions and decision-making processes, leading to a more
inclusive, sustainable, and healthier global community. Furthermore, this paper considers the ethical
dimensions of AI deployment, guided by UNESCO’s recommendations on AI ethics, which advocate
for transparency, accountability, and inclusivity in AI developments. It also examines the World
Economic Forum’s insights into AI’s potential to revolutionize learning and healthcare in underserved
populations, emphasizing the significance of fair AI advancements. By integrating perspectives from
prominent global organizations, this paper offers a strategic approach to combining AI with EQ,
enhancing the capacity of AI systems to meaningfully address global challenges. In conclusion, this
paper advocates for the establishment of a new Sustainable Development Goal, SDG 18, focused
on the ethical integration of AI and EQ across all sectors, ensuring that technology advances the
well-being of humanity and global sustainability.
Authors: Tomasz Wasilewski, Wojciech Kamysz and Jacek G˛ebicki
Abstract:
The steady progress in consumer electronics, together with improvement in microflow
techniques, nanotechnology, and data processing, has led to implementation of cost-effective, userfriendly portable devices, which play the.....
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Authors: Tomasz Wasilewski, Wojciech Kamysz and Jacek G˛ebicki
The steady progress in consumer electronics, together with improvement in microflow
techniques, nanotechnology, and data processing, has led to implementation of cost-effective, userfriendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover,
numerous smart devices monitor patients’ health, and some of them are applied in point-of-care
(PoC) tests as a reliable source of evaluation of a patient’s condition. Current diagnostic practices
are still based on laboratory tests, preceded by the collection of biological samples, which are then
tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting
passive/active physiological and behavioral data from patients in real time and feeding them to
artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis
and treatment procedures via the omission of conventional sampling and diagnostic procedures
while also excluding the role of pathologists. A combination of conventional and novel methods of
digital and traditional biomarker detection with portable, autonomous, and miniaturized devices
can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of
traditional clinical practices with modern diagnostic techniques based on AI and machine learning
(ML). The presented technologies will bypass laboratories and start being commercialized, which
should lead to improvement or substitution of current diagnostic tools. Their application in PoC
settings or as a consumer technology accessible to every patient appears to be a real possibility.
Research in this field is expected to intensify in the coming years. Technological advancements in
sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics
fields, fostering early disease detection and intervention strategies. The integration of AI with digital
health platforms would enable predictive analysis and personalized healthcare, emphasizing the
importance of interdisciplinary collaboration in related scientific fields.
Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing
(POCT) and wearable sensors for personalized medicine applications. This review explores the
recent advances and the transformative potential.....
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Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing
(POCT) and wearable sensors for personalized medicine applications. This review explores the
recent advances and the transformative potential of the use of AI in improving wearables and POCT.
The integration of AI significantly contributes to empowering these tools and enables continuous
monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare
efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and
non-invasive tracking of physiological conditions that are essential for early disease detection and
personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these
medical testing kits accessible and available even in resource-limited settings. This review discusses
the key advances in AI applications for data processing, sensor fusion, and multivariate analytics,
highlighting case examples that exhibit their impact in different medical scenarios. In addition, the
challenges associated with data privacy, regulatory approvals, and technology integrations into the
existing healthcare system have been overviewed. The outlook emphasizes the urgent need for
continued innovation in AI-driven health technologies to overcome these challenges and to fully
achieve the potential of these techniques to revolutionize personalized medicine.
Authors: Michele Avanzo, Joseph Stancanello, Giovanni Pirrone, Annalisa Drigo and Alessandra Retico
Abstract:
Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines
or computers the ability to perform human-like cognitive functions, began in the 1940s with the
first abstract.....
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Authors: Michele Avanzo, Joseph Stancanello, Giovanni Pirrone, Annalisa Drigo and Alessandra Retico
Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines
or computers the ability to perform human-like cognitive functions, began in the 1940s with the
first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning
algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent
advancements include the refinement of learning algorithms, the development of convolutional
neural networks to efficiently analyze images, and methods to synthesize new images. This renewed
enthusiasm was also due to the increase in computational power with graphical processing units
and the availability of large digital databases to be mined by neural networks. AI soon began to
be applied in medicine, first through expert systems designed to support the clinician’s decision
and later with neural networks for the detection, classification, or segmentation of malignant lesions
in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone
compared with a double reading by two radiologists on screening mammography. Natural language
processing, recurrent neural networks, transformers, and generative models have both improved
the capabilities of making an automated reading of medical images and moved AI to new domains,
including the text analysis of electronic health records, image self-labeling, and self-reporting. The
availability of open-source and free libraries, as well as powerful computing resources, has greatly
facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI
in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools
as ‘black boxes’ that require greater interpretability and explainability, and ethical issues related
to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive
results, AI is one of the most promising resources for frontier research and applications in medicine,
in particular for oncological applications.
Keywords: artificial intelligence; medical imaging; neural networks; machine learning; deep learning
The study presents a comprehensive framework for integrating foundation models (FMs),
federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft
health monitoring systems (AHMSs). The.....
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The study presents a comprehensive framework for integrating foundation models (FMs),
federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft
health monitoring systems (AHMSs). The proposed architecture uses the strengths of both centralized and decentralized learning approaches, combining the broad knowledge capture of foundation
models with the privacy-preserving and adaptive nature of federated learning. Through extensive simulations on a representative aircraft fleet, the integrated FM + FL approach demonstrated
consistently superior performance compared to standalone implementations across multiple key
metrics, including prediction accuracy, model size efficiency, and convergence speed. The framework
establishes a robust digital twin ecosystem for real-time monitoring, predictive maintenance, and
fleet-wide optimization. Comparative analysis reveals significant improvements in anomaly detection
capabilities and reduced false alarm rates compared to traditional methods. The study conducts a
systematic evaluation of the benefits and limitations of FM, FL, and integrated approaches in AHMS,
examining their implications for system robustness, scalability, and security. Statistical analysis confirms that the integrated approach substantially enhances precision and recall in identifying potential
failures while optimizing computational resources and training time. This paper outlines a detailed
aviation ecosystem architecture integrating these advanced AI technologies across centralized processing, client, and communication domains. Future research directions are identified, focusing
on improving model efficiency, ensuring generalization across diverse operational conditions, and
addressing regulatory and ethical considerations.
Keywords: aviation health monitoring systems; foundation models; federated learning; Artificial
Intelligence of Things; predictive maintenance; aircraft safety; machine learning in aviation; cognitive
computing
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