RS Global Journals
Not a member yet
3430 research outputs found
Sort by
ARTIFICIAL INTELLIGENCE IN TAX ENFORCEMENT: THE ROLE OF PERCEIVED AI CAPABILITY IN SHAPING TAX EVASION INTENTION
No full textThis study examines the effect of perceived artificial intelligence (AI) capability on tax evasion intention among corporate taxpayers in Indonesia. As digitalization and the adoption of AI in tax administration continue to expand, understanding how taxpayers cognitively respond to advanced technological surveillance has become increasingly important, particularly in developing country contexts. Using a quantitative explanatory design, data were collected through an online structured questionnaire administered to corporate tax decision-makers, yielding 278 valid responses. Hypotheses were tested using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS.
The empirical results indicate that perceived AI capability has a positive and significant effect on tax evasion intention, suggesting that the hypothesized negative relationship is not empirically supported. This finding implies that higher perceptions of AI-based surveillance capability do not automatically deter tax evasion intentions. Instead, they may encourage more adaptive and strategic responses in corporate tax planning. Corporate taxpayers appear to respond to sophisticated monitoring technologies by engaging in more complex risk evaluations rather than uniformly increasing compliance.
The study contributes to the tax behavior literature by integrating perceived AI capability as a technology-based psychological factor within the behavioral intention framework. From a practical perspective, the findings suggest that the implementation of AI in tax administration should be accompanied by policies emphasizing transparency, legal certainty, and clear risk communication to prevent strategic behavioral adaptation by corporate taxpayers
THE USE OF BOTULINUM TOXIN IN THE TREATMENT OF MIGRAINE
No full textIntroduction: Migraine is a chronic neurological disorder marked by recurrent moderate to severe headaches with symptoms such as nausea, photophobia, and phonophobia. Chronic migraine, occurring on 15 or more days per month for over three months, significantly impairs daily functioning and creates a substantial health burden. Botulinum toxin type A (BoNT-A) is an effective preventive option for patients who do not respond to standard treatments.
Aim of the study: The aim of this review is to summarize current evidence on the mechanism of action, clinical efficacy, safety profile, and practical use of botulinum toxin type A in the prevention of chronic migraine.
Materials and methods: A literature search was mostly performed in PubMed and Google Scholar for studies published between 2015 and 2025, using the keywords: migraine, chronic migraine, botulinum toxin, onabotulinumtoxinA, PREEMPT trials, CGRP. Priority was given to randomized controlled trials, long-term observational studies, clinical guidelines, and mechanistic research.
Discussion: OnabotulinumtoxinA (BoNT-A) is a well-established preventive treatment for chronic migraine supported by evidence from the PREEMPT trials and long-term studies such as COMPEL. BoNT-A significantly reduces headaches, improves quality of life and decreases disability with benefits sustained over multiple treatment cycles. Its mechanism blocking the release of CGRP, substance P, and glutamate from sensory nerves and modulating nociceptive receptors targets both peripheral and central sensitization, which distinguishes it from traditional oral medication.
Injection protocols vary worldwide. The PREEMPT paradigm is evidence-based and standardized, while alternative approaches, such as the Saudi 5/20/100 protocol, offer lower doses and fewer injections but lack of large-scale validation. The safety profile is generally positive. Following recommended dosing intervals minimizes the risk of neutralizing antibodies.
Emerging CGRP-targeting therapies provide additional options, and early data suggest potential benefits of combination therapy for refractory cases. Economic analyses indicate that despite higher upfront costs, BoNT-A reduces healthcare use and disability, making it cost-effective in the long term. Future research should focus on identifying predictors of response, optimizing injection protocols, and evaluating combination strategies with biologics.
Results: Evidence from large randomized trials (PREEMPT 1 and 2) demonstrates that BoNT-A significantly reduces the number of headache days, improves quality of life, and decreases disability in patients with chronic migraine. Long-term studies show sustained benefits over multiple treatment cycles with a favorable safety profile. BoNT-A reduces peripheral and central sensitization by inhibiting the release of pain-related neuropeptides and modulating sensory nerve activity.
Conclusion: OnabotulinumtoxinA is an effective and well-tolerated preventive treatment for chronic migraine. Standardized injection protocols and appropriate patient selection optimize therapeutic outcomes. Further research is needed to identify predictors of treatment response and to explore the potential of combination therapy with CGRP (calcitonin gene-related peptide)-targeting agents
EXPLAINABLE ARTIFICIAL INTELLIGENCE IN HEALTHCARE: FROM ALGORITHMIC TRANSPARENCY TO TRUST AND SOCIAL ACCEPTANCE IN CLINICAL PRACTICE
No full textBackground and objective: The rapid expansion of artificial intelligence (AI) in healthcare has resulted in substantial advances in diagnostics, prognostics, clinical decision support systems, and patient monitoring. Despite promising performance, many AI-based systems remain insufficiently understood by clinicians and patients due to their “black-box” nature. This lack of transparency may undermine trust, hinder acceptance, and limit safe integration into routine clinical practice. Explainable artificial intelligence (XAI) has emerged as a response to these challenges by enabling human-interpretable explanations of algorithmic decisions. The objective of this narrative review is to synthesize current evidence on XAI in healthcare, with particular emphasis on its technical foundations, clinical applications, influence on trust and decision-making, and broader social and ethical implications.
Scope of review: This review synthesizes literature published between 2019 and 2025 addressing explainability in medical AI. The analysis includes methodological studies, clinical evaluations, human–computer interaction research, and social science investigations related to transparency, trust, accountability, and acceptance of AI systems. Relevant publications were identified through structured searches of PubMed, MEDLINE, Scopus, and Google Scholar using keywords related to explainable AI, interpretability, ethics, trust, and clinical decision support.
Findings: XAI methods—including feature attribution, model simplification, counterfactual explanations, and visualization techniques—demonstrate potential to enhance clinician understanding of AI outputs and increase confidence in algorithm-assisted decisions. Evidence suggests that explainability may support diagnostic accuracy, reduce automation bias, and facilitate error detection. However, explainability alone does not ensure trust. Clinical context, user expertise, organizational culture, and regulatory frameworks play critical roles in shaping the adoption and appropriate use of explainable systems. Empirical research addressing patient perspectives remains limited.
Conclusions: Explainable AI constitutes an important step toward the responsible and socially acceptable integration of intelligent systems in healthcare. While XAI can enhance transparency and trust, its effectiveness depends on thoughtful design, contextual adaptation to clinical workflows, and alignment with user needs. Further interdisciplinary research is required to standardize explainability approaches, evaluate their real-world impact on clinical outcomes, and address the ethical, legal, and societal challenges associated with medical AI
THE EXPANDING ROLE OF ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSIS AND TREATMENT: IMPACTS ON CLINICAL QUALITY, PATIENT OUTCOMES, AND THE FUTURE OF PHYSICIAN EMPLOYMENT – A REVIEW
No full textArtificial intelligence (AI) is rapidly transforming medical diagnosis and treatment, presenting significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and enable more personalized, data-driven patient care. This narrative review synthesizes contemporary evidence on the expanding applications of AI in diagnostic interpretation, therapeutic decision-making, and clinical management, while also examining its broader implications for healthcare quality, patient outcomes, and the future of physician employment. A structured review methodology was applied, incorporating 54 peer-reviewed studies published between 2015 and 2025 across key medical domains, including radiology, oncology, chronic disease management, predictive analytics, and healthcare workforce transformation. The reviewed literature demonstrates that AI-based systems can equal or exceed clinician performance in selected, well-defined diagnostic tasks, contributing to earlier disease detection, improved risk stratification, and more optimized treatment planning. At the same time, the translation of these technologies into routine clinical practice remains constrained by persistent challenges, including algorithmic bias, limited transparency and explainability, regulatory uncertainty, data privacy concerns, and variability in real-world performance. Beyond clinical outcomes, the growing automation of cognitive tasks traditionally performed by physicians raises important questions regarding professional deskilling, evolving role boundaries, and the reconfiguration of medical labor. While AI is unlikely to fully replace clinicians, its continued integration is expected to substantially reshape clinical responsibilities, interdisciplinary collaboration, and required skill sets. Overall, this review underscores the dual impact of AI as both a driver of improved clinical quality and a catalyst for structural change within the medical profession, and identifies key research and policy priorities necessary to ensure its safe, ethical, and equitable implementation
ARTIFICIAL INTELLIGENCE IN CARDIOLOGY: APPLICATIONS ACROSS ELECTROCARDIOGRAPHY AND CARDIOVASCULAR IMAGING
No full textObjective: The objective of this narrative review was to summarize and critically appraise current clinical applications of artificial intelligence (AI) in cardiology, with particular emphasis on arrhythmia detection and cardiovascular imaging modalities.
Methods: A selective narrative review of peer-reviewed literature published between 2015 and 2025 was performed using PubMed, Scopus, and Web of Science. Studies were selected based on clinical relevance, validation methodology, and reported diagnostic or prognostic performance in human populations.
Results: AI-based algorithms demonstrate diagnostic performance comparable to expert interpretation in atrial fibrillation (AF) detection as well as across echocardiography, coronary computed tomography angiography (CCTA), cardiac magnetic resonance imaging (CMR), and intravascular optical coherence tomography (OCT). Beyond diagnostic accuracy, AI has been shown to reduce analysis time and interobserver variability in controlled and retrospective study settings. However, the majority of available studies are retrospective, rely on curated datasets, and lack prospective validation demonstrating a direct impact on clinical outcomes.
Conclusions: AI constitutes a valuable decision-support tool in contemporary cardiology, enhancing diagnostic efficiency and risk stratification across multiple imaging and electrophysiological modalities. Nevertheless, broader clinical adoption will require rigorous external validation, improved model interpretability, and prospective outcome-driven studies. This review uniquely integrates evidence across electrophysiology and multiple imaging modalities to identify shared translational challenges and near-term clinical opportunities for AI in cardiology
THE "FITNESS AGE" CONSTRUCT IN CONSUMER WEARABLES: A CRITICAL REVIEW OF PHYSIOLOGICAL VALIDITY AND THE PSYCHOSOCIAL IMPACT ON CARDIOVASCULAR PATIENT IDENTITY
No full textBackground. Consumer wearables increasingly translate complex physiological data into simplified constructs intended for everyday users. One of the most influential of these is “Fitness Age” (FA), a proprietary metric primarily derived from estimated VO₂ max, resting heart rate, and activity patterns. Although widely adopted by patients and recreational athletes, its clinical validity and psychosocial consequences remain insufficiently examined, particularly in cardiovascular populations.
Objective. This review critically evaluates the physiological foundations of the Fitness Age construct and explores its impact on patient health identity and illness perception, with particular relevance for cardiovascular care.
Methods. A systematic review was conducted in accordance with PRISMA 2020 guidelines, covering publications from 2015 to 2026. Physiological validation studies comparing wearable-derived metrics with clinical gold standards (CPET, ECG, Holter monitoring) were analyzed alongside qualitative and quantitative research addressing psychosocial outcomes.
Results. High-end Garmin wearables demonstrate strong accuracy for resting heart rate and nocturnal heart rate variability, while estimated VO₂ max shows a consistent error margin of approximately 5–8% in clinical cohorts. Psychosocially, Fitness Age functions as a powerful motivational tool but may also contribute to algorithm-driven anxiety and altered patient identity, particularly in individuals with established cardiovascular disease.
Conclusions. Fitness Age should be interpreted as a behavioral and motivational proxy rather than a diagnostic indicator. Clinicians must actively contextualize wearable-derived metrics to harness their preventive potential while minimizing psychological harm
ARTIFICIAL INTELLIGENCE IN ANESTHETIC DRUG DELIVERY AND HEMODYNAMIC CONTROL: CLOSED-LOOP SYSTEMS, PREDICTION ALGORITHMS, AND A PRACTICAL IMPLEMENTATION APPROACH
No full textModern anesthesia is a high-frequency control problem: clinicians must continuously titrate hypnotics, opioids, fluids, and vasopressors to achieve adequate hypnosis/analgesia while avoiding hemodynamic instability and downstream organ injury. Artificial intelligence (AI), machine learning, and classical control engineering are increasingly embedded in perioperative monitors and drug delivery platforms, enabling decision support and closed-loop control. Randomized trials and meta-analyses indicate that closed-loop systems for hypnosis (typically processed EEG targets such as BIS) improve time-in-target and reduce overshoot/undershoot compared with manual titration. Multi-variable systems that co-manage hypnosis, analgesia, and fluids are feasible and may improve short-term recovery outcomes in selected settings. On the hemodynamic side, intraoperative hypotension is common and associated with myocardial and kidney injury, AI-based early warning systems using arterial waveforms can predict hypotension minutes before onset and, when paired with treatment protocols, may reduce hypotension burden in some trials. Closed-loop vasopressor and fluid systems improve protocol adherence and reduce hypotension in perioperative and early postoperative care. AI-enabled decision support and closed-loop controllers can improve stability of anesthesia and blood pressure management, but they should be implemented as supervised systems with clear safety constraints, manual override, and ongoing performance monitoring. Future multicenter trials should prioritize patient-centered outcomes, external validation, and transparent reporting
ADVERSE EFFECTS OF ESKETAMINE IN TREATMENT RESISTANT DEPRESSION: A COMPREHENSIVE LITERATURE REVIEW (2020-2025)
No full textBackground: Esketamine nasal spray represents the first FDA approved treatment with a novel mechanism of action for treatment resistant depression (TRD) in decades. While its efficacy has been well established, a thorough understanding of the adverse effect profile remains essential for informed clinical decision making and optimal patient safety. We therefore undertook this systematic review to characterize the safety profile of esketamine in treatment resistant depression.
Aim: This review examines esketamine's adverse effect profile, focusing on common effects, cardiovascular safety, urological considerations, cognitive outcomes, abuse liability, special populations, and serious adverse events.
Materials and Methods: We performed a comprehensive literature search across multiple databases including PubMed, Cochrane Library, Web of Science, Embase, Google Scholar, and MEDLINE for studies published 2020-2025.
Results: Common adverse effects like dissociation and sedation resolved within two hours. Blood pressure elevations normalized within 1.5 hours without intervention. Pre approval concerns were not confirmed: no bladder cystitis occurred despite years of exposure, cognitive function remained stable or improved, and misuse was rare (less than 0.01%). Serious adverse events were infrequent (less than 0.2% of sessions), occurring mainly during initial treatments. Mortality rates matched background rates in treatment resistant depression. Elderly patients showed similar tolerability but needed closer cardiovascular monitoring.
Conclusion: Under proper clinical supervision, esketamine shows an acceptable safety profile. Most adverse effects are temporary and mild to moderate. Long term studies extending up to 6.5 years found no evidence of organ damage, cognitive decline, or meaningful abuse problems. For patients who have failed multiple treatments, esketamine provides an important alternative with manageable safety concerns
ANTIDEPRESSANT-ASSOCIATED SEXUAL DYSFUNCTION (AASD) – FROM PATHOPHYSIOLOGY TO CLINICAL MANAGEMENT: A LITERATURE REVIEW
No full textMajor Depressive Disorder (MDD) presents a global challenge, with pharmacotherapy based on SSRIs and SNRIs remaining the standard of care. Despite clinical efficacy, the tolerability of these agents is often limited by iatrogenic Antidepressant-Associated Sexual Dysfunction (AASD), a leading cause of therapeutic non-adherence and so-called "hidden non-compliance," resulting in disease relapse. This paper provides a comprehensive literature review aimed at updating knowledge on epidemiology, neurobiological mechanisms extending beyond the serotonin hypothesis, and modern management strategies for AASD, with particular emphasis on Post-SSRI Sexual Dysfunction (PSSD). Databases including PubMed, ScienceDirect, Cochrane Library, and Google Scholar were searched, focusing on studies utilizing validated psychometric tools (e.g., ASEX, PRSexDQ). The analysis reveals a significant disparity between spontaneously reported AASD rates (10–15%) and those detected via screening (70–80%). A risk hierarchy was confirmed: paroxetine and venlafaxine demonstrate the highest iatrogenic potential, attributed partly to nitric oxide synthase inhibition and dopaminergic suppression. Vortioxetine and bupropion are characterized by a neutral profile, offering a safe alternative. The review also confirms the growing clinical importance of PSSD as a persistent complication. AASD requires proactive monitoring in daily practice. Results support moving away from a "wait and see" strategy in favor of routine screening and early implementation of switching or augmentation strategies. Personalization of pharmacotherapy is crucial for maintaining patient adherence and ensuring long-term depression treatment efficacy
THE EFFECTIVENESS OF THE IMPLEMENTATION OF FINE PENALTIES IN NARCOTICS CRIMES FOLLOWING LAW NUMBER 01 OF 2023 CONCERNING THE CRIMINAL CODE (A FIELD STUDY IN THE JURISDICTIONAL REGION OF KEDIRI REGENCY)
No full textThe goal of this research is to investigate the factors that judges take into account when imposing fines on individuals convicted of narcotics offenses under Article 112 of Law Number 35 of 2009 on Narcotics. It also seeks to explore how fine penalties are carried out if the offender is unable to make the payment after the implementation of Law Number 1 of 2023 on the Criminal Code. This research employs a socio-legal method with conceptual, statutory, and case approaches, as well as descriptive qualitative analysis through observation, interviews, document studies, and literature review in the jurisdictional area of the Kediri Regency District Court. The results show that the application of fine penalties still does not align with the principle of proportionality. Judges prioritize consideration of the defendant's economic capacity without adequate juridical reasoning, resulting in the majority of convicts opting for a relatively short substitute imprisonment term rather than paying an exceedingly high fine. This condition causes the objectives of sentencing, particularly deterrence, to remain unachieved. Furthermore, there is no mechanism for coercive measures to ensure the execution of fine penalties. In conclusion, the imposition of fine penalties in narcotics cases under Article 112 is ineffective and disproportionate, necessitating the strengthening of the fine execution mechanism in accordance with the provisions of Law No. 1 of 2023 to ensure more just and optimal sentencing