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1.
J Med Syst ; 48(1): 59, 2024 Jun 05.
Article in English | MEDLINE | ID: mdl-38836893

ABSTRACT

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach.


Subject(s)
Artificial Intelligence , Humans , Clinical Decision-Making/methods , Precision Medicine/methods , Education, Medical/methods
4.
J Med Internet Res ; 26: e50274, 2024 Jun 06.
Article in English | MEDLINE | ID: mdl-38842929

ABSTRACT

Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.


Subject(s)
Artificial Intelligence , United States Food and Drug Administration , United States , Humans , Drug-Related Side Effects and Adverse Reactions , Clinical Decision-Making , Product Surveillance, Postmarketing/methods , Adverse Drug Reaction Reporting Systems , Algorithms , Trust
6.
World J Gastroenterol ; 30(20): 2726-2730, 2024 May 28.
Article in English | MEDLINE | ID: mdl-38855153

ABSTRACT

The screening of colorectal cancer (CRC) is pivotal for both the prevention and treatment of this disease, significantly improving early-stage tumor detection rates. This advancement not only boosts survival rates and quality of life for patients but also reduces the costs associated with treatment. However, the adoption of CRC screening methods faces numerous challenges, including the technical limitations of both noninvasive and invasive methods in terms of sensitivity and specificity. Moreover, socioeconomic factors such as regional disparities, economic conditions, and varying levels of awareness affect screening uptake. The coronavirus disease 2019 pandemic further intensified these cha-llenges, leading to reduced screening participation and increased waiting periods. Additionally, the growing prevalence of early-onset CRC necessitates innovative screening approaches. In response, research into new methodologies, including artificial intelligence-based systems, aims to improve the precision and accessibility of screening. Proactive measures by governments and health organizations to enhance CRC screening efforts are underway, including increased advocacy, improved service delivery, and international cooperation. The role of technological innovation and global health collaboration in advancing CRC screening is undeniable. Technologies such as artificial intelligence and gene sequencing are set to revolutionize CRC screening, making a significant impact on the fight against this disease. Given the rise in early-onset CRC, it is crucial for screening strategies to continually evolve, ensuring their effectiveness and applicability.


Subject(s)
COVID-19 , Colorectal Neoplasms , Early Detection of Cancer , Humans , Colorectal Neoplasms/diagnosis , Colorectal Neoplasms/epidemiology , Early Detection of Cancer/methods , COVID-19/diagnosis , COVID-19/epidemiology , Artificial Intelligence , Mass Screening/methods , Mass Screening/organization & administration , SARS-CoV-2/isolation & purification , Quality of Life , Colonoscopy
7.
Eur Rev Med Pharmacol Sci ; 28(10): 3542-3547, 2024 May.
Article in English | MEDLINE | ID: mdl-38856129

ABSTRACT

From a clinical viewpoint, there are enormous obstacles to early detection and diagnosis as well as treatment interventions for multiple sclerosis (MS). With the growing application of methods based on artificial intelligence (AI) to medical problems, there might be an opportunity for MS specialists and their patients. However, to develop accurate AI models, researchers must first examine large quantities of patient data (demographics, genetics-based information, clinical and radiological presentation) to identify the characteristics that distinguish illness from health. These are seen as promising approaches toward improved disease diagnosis, treatment individualization, and prognosis prediction. When applied to imaging data, the application of AI subdomains, such as machine learning (ML), deep learning (DL), and neural networks, have proven their value in healthcare. The application of AI in MS management marks a milestone within the healthcare sector. Now, as research and applications of AI continue to advance steadily, breakthroughs are coming at an ever-accelerating pace. As MS continues to develop, the integration of AI is more and more necessary for continuing progress in diagnosis and treatment as well as patient outcomes. In the field of multiple sclerosis, these algorithms have been used for many purposes, such as disease monitoring and therapy.


Subject(s)
Artificial Intelligence , Multiple Sclerosis , Humans , Multiple Sclerosis/therapy , Multiple Sclerosis/diagnosis , Deep Learning , Neural Networks, Computer , Machine Learning
8.
Zhonghua Jie He He Hu Xi Za Zhi ; 47(6): 566-570, 2024 Jun 12.
Article in Chinese | MEDLINE | ID: mdl-38858209

ABSTRACT

Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates for early-stage lung cancers (stages IA1, IA2, IA3, and IB, according to the TNM staging system) are significantly higher, underscoring the critical importance of early detection, diagnosis, and treatment. Ground-glass nodules (GGNs), which are commonly seen on lung imaging, can be indicative of both benign and malignant lesions. For clinicians, accurately characterizing GGNs and choosing the right management strategies present significant challenges. Artificial intelligence (AI), specifically deep learning algorithms, has shown promise in the evaluation of GGNs by analyzing complex imaging data and predicting the nature of GGNs, including their benign or malignant status, pathological subtypes, and genetic mutations such as epidermal growth factor receptor (EGFR) mutations. By integrating imaging features and clinical data, AI models have demonstrated high accuracy in distinguishing between benign and malignant GGNs and in predicting specific pathological subtypes. In addition, AI has shown promise in predicting genetic mutations such as EGFR mutations, which are critical for personalized treatment decisions in lung cancer. While AI offers significant potential to improve the accuracy and efficiency of GGN assessment, challenges remain, such as the need for extensive validation studies, standardization of imaging protocols, and improving the interpretability of AI algorithms. In summary, AI has the potential to revolutionise the management of GGNs by providing clinicians with more accurate and timely information for diagnosis and treatment decisions. However, further research and validation are needed to fully realize the benefits of AI in clinical practice.


Subject(s)
Artificial Intelligence , Lung Neoplasms , Humans , Lung Neoplasms/diagnosis , Lung Neoplasms/pathology , Lung Neoplasms/diagnostic imaging , Algorithms , Tomography, X-Ray Computed/methods , Lung/pathology , Lung/diagnostic imaging , Deep Learning , Multiple Pulmonary Nodules/diagnosis , Multiple Pulmonary Nodules/diagnostic imaging
9.
J Health Organ Manag ; ahead-of-print(ahead-of-print)2024 Jun 11.
Article in English | MEDLINE | ID: mdl-38858220

ABSTRACT

PURPOSE: This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educational curricula and suggests enhancements to better prepare future healthcare professionals for the evolving demands of AI-driven healthcare environments. DESIGN/METHODOLOGY/APPROACH: This study utilized a cross-sectional survey design to understand healthcare administration students' perceptions regarding integrating AI in healthcare leadership. An online questionnaire, developed from an extensive literature review covering fundamental AI knowledge and its role in sustainable leadership, was distributed to students majoring and minoring in healthcare administration. This methodological approach garnered participation from 62 students, providing insights and perspectives crucial for the study's objectives. FINDINGS: The research revealed that while a significant majority of healthcare administration students (70%) recognize the potential of AI in fostering sustainable leadership in healthcare, only 30% feel adequately prepared to work in AI-integrated environments. Additionally, students were interested in learning more about AI applications in healthcare and the role of AI in sustainable leadership, underscoring the need for comprehensive AI-focused education in their curriculum. RESEARCH LIMITATIONS/IMPLICATIONS: The research is limited by its focus on a single academic institution, which may not fully represent the diversity of perspectives in healthcare administration. PRACTICAL IMPLICATIONS: This study highlights the need for healthcare administration curricula to incorporate AI education, aligning theoretical knowledge with practical applications, to effectively prepare future professionals for the evolving demands of AI-integrated healthcare environments. ORIGINALITY/VALUE: This research paper presents insights into healthcare administration students' readiness and perspectives toward AI integration in healthcare leadership, filling a critical gap in understanding the educational needs in the evolving landscape of AI-driven healthcare.


Subject(s)
Artificial Intelligence , Leadership , Humans , Cross-Sectional Studies , Surveys and Questionnaires , Male , Female , Adult , Young Adult , Curriculum
10.
Radiology ; 311(3): e241222, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38860895
11.
BMC Bioinformatics ; 25(1): 208, 2024 Jun 08.
Article in English | MEDLINE | ID: mdl-38849719

ABSTRACT

BACKGROUND: Drug design is a challenging and important task that requires the generation of novel and effective molecules that can bind to specific protein targets. Artificial intelligence algorithms have recently showed promising potential to expedite the drug design process. However, existing methods adopt multi-objective approaches which limits the number of objectives. RESULTS: In this paper, we expand this thread of research from the many-objective perspective, by proposing a novel framework that integrates a latent Transformer-based model for molecular generation, with a drug design system that incorporates absorption, distribution, metabolism, excretion, and toxicity prediction, molecular docking, and many-objective metaheuristics. We compared the performance of two latent Transformer models (ReLSO and FragNet) on a molecular generation task and show that ReLSO outperforms FragNet in terms of reconstruction and latent space organization. We then explored six different many-objective metaheuristics based on evolutionary algorithms and particle swarm optimization on a drug design task involving potential drug candidates to human lysophosphatidic acid receptor 1, a cancer-related protein target. CONCLUSION: We show that multi-objective evolutionary algorithm based on dominance and decomposition performs the best in terms of finding molecules that satisfy many objectives, such as high binding affinity and low toxicity, and high drug-likeness. Our framework demonstrates the potential of combining Transformers and many-objective computational intelligence for drug design.


Subject(s)
Algorithms , Drug Design , Humans , Molecular Docking Simulation , Receptors, Lysophosphatidic Acid/metabolism , Receptors, Lysophosphatidic Acid/chemistry , Artificial Intelligence
12.
BMC Cancer ; 24(1): 705, 2024 Jun 08.
Article in English | MEDLINE | ID: mdl-38849731

ABSTRACT

BACKGROUND: Despite recent improvements in cancer detection and survival rates, managing cancer-related pain remains a significant challenge. Compared to neuropathic and inflammatory pain conditions, cancer pain mechanisms are poorly understood, despite pain being one of the most feared symptoms by cancer patients and significantly impairing their quality of life, daily activities, and social interactions. The objective of this work was to select a panel of biomarkers of central pain processing and modulation and assess their ability to predict chronic pain in patients with cancer using predictive artificial intelligence (AI) algorithms. METHODS: We will perform a prospective longitudinal cohort, multicentric study involving 450 patients with a recent cancer diagnosis. These patients will undergo an in-person assessment at three different time points: pretreatment, 6 months, and 12 months after the first visit. All patients will be assessed through demographic and clinical questionnaires and self-report measures, quantitative sensory testing (QST), and electroencephalography (EEG) evaluations. We will select the variables that best predict the future occurrence of pain using a comprehensive approach that includes clinical, psychosocial, and neurophysiological variables. DISCUSSION: This study aimed to provide evidence regarding the links between poor pain modulation mechanisms at precancer treatment in patients who will later develop chronic pain and to clarify the role of treatment modality (modulated by age, sex and type of cancer) on pain. As a final output, we expect to develop a predictive tool based on AI that can contribute to the anticipation of the future occurrence of pain and help in therapeutic decision making.


Subject(s)
Cancer Pain , Chronic Pain , Humans , Chronic Pain/diagnosis , Chronic Pain/etiology , Prospective Studies , Cancer Pain/diagnosis , Female , Male , Longitudinal Studies , Neoplasms/complications , Biomarkers , Pain Measurement/methods , Quality of Life , Artificial Intelligence , Electroencephalography , Adult , Middle Aged
13.
BMC Med Educ ; 24(1): 644, 2024 Jun 07.
Article in English | MEDLINE | ID: mdl-38849847

ABSTRACT

BACKGROUND: The rapid growth of artificial intelligence (AI) technologies has been driven by the latest advances in computing power. Although, there exists a dearth of research on the application of AI in medical education. METHODS: this study is based on the TAM-ISSM-UTAUT model and introduces STARA awareness and chilling effect as moderating variables. A total of 657 valid questionnaires were collected from students of a medical university in Dalian, China, and data were statistically described using SPSS version 26, Amos 3.0 software was used to validate the research model, as well as moderated effects analysis using Process (3.3.1) software, and Origin (2021) software. RESULTS: The findings reveal that both information quality and perceived usefulness are pivotal factors that positively influence the willingness to use AI products. It also uncovers the moderating influence of the chilling effect and STARA awareness. CONCLUSIONS: This suggests that enhancing information quality can be a key strategy to encourage the widespread use of AI products. Furthermore, this investigation offers valuable insights into the intersection of medical education and AI use from the standpoint of medical students. This research may prove to be pertinent in shaping the promotion of Medical Education Intelligence in the future.


Subject(s)
Artificial Intelligence , Education, Medical , Students, Medical , Humans , Students, Medical/psychology , Surveys and Questionnaires , Male , Female , China , Young Adult , Awareness
14.
Recenti Prog Med ; 115(6): 300-301, 2024 Jun.
Article in Italian | MEDLINE | ID: mdl-38853735
15.
Radiat Oncol ; 19(1): 69, 2024 May 31.
Article in English | MEDLINE | ID: mdl-38822385

ABSTRACT

BACKGROUND: Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice, three commercially available CT-based solutions were evaluated. MATERIALS AND METHODS: The following solutions were evaluated in this work: MIM-ProtégéAI+ (MIM), Radformation-AutoContour (RAD), and Siemens-DirectORGANS (SIE). Sixteen organs were identified that could be contoured by all solutions. For each organ, ten patients that had manually generated contours approved by the treating physician (AP) were identified, totaling forty-seven different patients. CT scans in the supine position were acquired using a Siemens-SOMATOMgo 64-slice helical scanner and used to generate autocontours. Physician scoring of contour accuracy was performed by at least three physicians using a five-point Likert scale. Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean distance to agreement (MDA) were calculated comparing AI contours to "ground truth" AP contours. RESULTS: The average physician score ranged from 1.00, indicating that all physicians reviewed the contour as clinically acceptable with no modifications necessary, to 3.70, indicating changes are required and that the time taken to modify the structures would likely take as long or longer than manually generating the contour. When averaged across all sixteen structures, the AP contours had a physician score of 2.02, MIM 2.07, RAD 1.96 and SIE 1.99. DSC ranged from 0.37 to 0.98, with 41/48 (85.4%) contours having an average DSC ≥ 0.7. Average HD ranged from 2.9 to 43.3 mm. Average MDA ranged from 0.6 to 26.1 mm. CONCLUSIONS: The results of our comparison demonstrate that each vendor's AI contouring solution exhibited capabilities similar to those of manual contouring. There were a small number of cases where unusual anatomy led to poor scores with one or more of the solutions. The consistency and comparable performance of all three vendors' solutions suggest that radiation oncology centers can confidently choose any of the evaluated solutions based on individual preferences, resource availability, and compatibility with their existing clinical workflows. Although AI-based contouring may result in high-quality contours for the majority of patients, a minority of patients require manual contouring and more in-depth physician review.


Subject(s)
Artificial Intelligence , Radiotherapy Planning, Computer-Assisted , Tomography, X-Ray Computed , Humans , Radiotherapy Planning, Computer-Assisted/methods , Organs at Risk/radiation effects , Algorithms , Image Processing, Computer-Assisted/methods
17.
J Health Commun ; 29(6): 396-399, 2024 Jun 02.
Article in English | MEDLINE | ID: mdl-38832662

ABSTRACT

There is strong evidence of the impact of opinion leaders in health promotion programs. Early work by Burke-Garcia suggests that social media influencers are the opinion leaders of the digital age as they come from the communities they influence, have built trust with them, and may be useful in combating misinformation by disseminating credible and timely health information and prompting consideration of health behaviors. AI has contributed to the spread of misinformation, but it can also be a vital part of the solution, informing and educating in real time and at scale. Personalized, empathetic messaging is crucial, though, and research supports that individuals are drawn to empathetic AI responses and prefer them to human responses in some digital environments. This mimics what we know about influencers and how they approach communicating with their followers. Blending what we know about social media influencers as opinion leaders with the power and scale of AI can enable us to address the spread of misinformation. This paper reviews the knowledge base and proposes the development of something we term "Health Communication AI" - perhaps the newest form of opinion leader - to fight health misinformation.


Subject(s)
Artificial Intelligence , Communication , Health Communication , Leadership , Social Media , Humans , Health Communication/methods
18.
Sci Rep ; 14(1): 13122, 2024 06 07.
Article in English | MEDLINE | ID: mdl-38849417

ABSTRACT

Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases ( n = 9854 ECGs) and achieved an F1 score of 95.43% in AF versus non-AF classification on an unseen ECG test dataset. A systematic comparison of xAI methods showed that deep Taylor decomposition provided the most trustworthy explanations ( + 24 % compared to the second-best approach). xECGArch can account for short- and long-term features corresponding to clinical features of morphology and rhythm, respectively. Further research will focus on the relationship between xECGArch features and clinical features, which may help in medical applications for diagnosis and therapy.


Subject(s)
Atrial Fibrillation , Deep Learning , Electrocardiography , Neural Networks, Computer , Electrocardiography/methods , Humans , Atrial Fibrillation/diagnosis , Atrial Fibrillation/physiopathology , Databases, Factual , Artificial Intelligence , Algorithms
19.
Sci Rep ; 14(1): 13162, 2024 06 07.
Article in English | MEDLINE | ID: mdl-38849439

ABSTRACT

Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis and Xpert MTB/RIF assay cycle threshold (Ct) values. In this retrospective study across six South Korean referral centers (January 1 to December 31, 2019), we included adults with rifampicin-susceptible pulmonary tuberculosis confirmed by Xpert assay from sputum samples. We analyzed patient characteristics, AI-based tuberculosis extent scores from chest X-rays, and Xpert Ct values. Of 230 patients, 206 (89.6%) achieved treatment success. The median age was 61 years, predominantly male (76.1%). AI-based radiographic tuberculosis extent scores (median 7.5) significantly correlated with treatment success (odds ratio [OR] 0.938, 95% confidence interval [CI] 0.895-0.983) and culture conversion at 8 weeks (liquid medium: OR 0.911, 95% CI 0.853-0.973; solid medium: OR 0.910, 95% CI 0.850-0.973). Sputum smear positivity was 49.6%, with a median Ct of 26.2. However, Ct values did not significantly correlate with major treatment outcomes. AI-based radiographic scoring at diagnosis is a significant predictor of treatment success and culture conversion in pulmonary tuberculosis, underscoring its potential in personalized patient management.


Subject(s)
Artificial Intelligence , Sputum , Tuberculosis, Pulmonary , Humans , Male , Female , Middle Aged , Tuberculosis, Pulmonary/drug therapy , Tuberculosis, Pulmonary/diagnostic imaging , Retrospective Studies , Treatment Outcome , Aged , Sputum/microbiology , Adult , Mycobacterium tuberculosis/drug effects , Mycobacterium tuberculosis/isolation & purification , Rifampin/therapeutic use , Republic of Korea , Tomography, X-Ray Computed/methods , Antitubercular Agents/therapeutic use , Radiography, Thoracic/methods
20.
Sci Rep ; 14(1): 13186, 2024 06 08.
Article in English | MEDLINE | ID: mdl-38851769

ABSTRACT

Social facilitation is a well-known phenomenon where the presence of organisms belonging to the same species enhances an individual organism's performance in a specific task. As far as fishes are concerned, most studies on social facilitation have been conducted in standing-water conditions. However, for riverine species, fish are most commonly located in moving waters, and the effects of hydrodynamics on social facilitation remain largely unknown. To bridge this knowledge gap, we designed and performed flume experiments where the behaviour of wild juvenile Italian riffle dace (Telestes muticellus) in varying group sizes and at different mean flow velocities, was studied. An artificial intelligence (AI) deep learning algorithm was developed and employed to track fish positions in time and subsequently assess their exploration, swimming activity, and space use. Results indicate that energy-saving strategies dictated space use in flowing waters regardless of group size. Instead, exploration and swimming activity increased by increasing group size, but the magnitude of this enhancement (which quantifies social facilitation) was modulated by flow velocity. These results have implications for how future research efforts should be designed to understand the social dynamics of riverine fish populations, which can no longer ignore the contribution of hydrodynamics.


Subject(s)
Exploratory Behavior , Swimming , Animals , Swimming/physiology , Exploratory Behavior/physiology , Behavior, Animal/physiology , Hydrodynamics , Fishes/physiology , Artificial Intelligence , Water Movements , Social Behavior
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