Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin

Integrating enterprise risk management to address AI-related risks in healthcare: Strategies for effective risk mitigation and implementation

The incorporation of artificial intelligence (AI) in health care offers revolutionary enhancements in patient diagnostics, clinical processes, and overall access to services. Nevertheless, this technological transition brings forth various new, intricate risks that pose challenges to current safety and ethical norms. This research explores the ability of enterprise risk management as an all-encompassing framework to tackle these arising risks, providing both a forward-looking and responsive strategy designed for the health care industry. At the core of this method are instruments that together seek to proactively uncover and address AI-related weaknesses like algorithmic bias, system failures, and data privacy issues. On the reactive side, it incorporates incident reporting systems and root cause analysis, tools that enable health care providers to quickly address unexpected events and consistently improve AI implementation procedures. However, some application difficulties still exist. The unclear, "black box" characteristics of numerous AI models hinder transparency and responsibility, prompting inquiries about the clarity of AI-generated choices and their adherence to ethical benchmarks in patient treatment. The research highlights that with the progress of AI technologies, the enterprise risk management framework also needs to evolve, addressing these new complexities while promoting a culture focused on safety in health care settings.

Evaluation of Health Science Students' Knowledge, Attitudes, and Practices Toward Artificial Intelligence in Northern Saudi Arabia: Implications for Curriculum Refinement and Healthcare Delivery

Background and Aim

As the integration of artificial intelligence (AI) in healthcare delivery becomes increasingly prevalent, understanding the knowledge, attitudes, and practices of health science students towards AI is crucial. However, limited evidence exists regarding the readiness of health science students, particularly in northern Saudi Arabia (KSA), to integrate AI into their future practices, highlighting the need for focused evaluation. We evaluated northern Saudi health science students' knowledge, attitude, practice, and associated factors toward AI.

Participants and Methods

The present cross-sectional study was conducted among 384 health science students aged 18 years and above from Jouf University, KSA. The study employed a validated data collection form with four sections: demographics, knowledge (AI principles and applications), attitudes (perceptions and ethical concerns), and practices (usage and confidence in AI tools). The three domains' scores were categorized as low (<60%), medium (60-80%) and high (>80%) based on their total scores. We utilized Spearman correlation test to ascertain the strength and direction of correlation among each subscale. Additionally, multivariate analysis was employed to identify associated factors.

Results

The present study demonstrated low knowledge, attitude, and practices among 55.7%, 37.0%, and 50.3% of health science students. We observed a positive correlation between knowledge and attitude (rho = 0.451, p = 0.001), knowledge and practice (rho = 0.353, p = 0.001), and attitude and practice (rho = 0.651, p = 0.001). Knowledge (p = 0.001) and practice (p = 0.002) were significantly higher among the students who participated in a formal AI training program. Females had a significantly higher level of attitude (p = 0.001) and practice (p = 0.030) than males.

Conclusion

In light of these findings, refining the curriculum to incorporate AI emerges as a critical strategy for addressing gaps in AI knowledge, attitudes, and practices among health science students. Therefore, formal and integrated training programs tailored to suit the local setting can effectively prepare health science students to adopt AI technologies in ways that enhance patient care.

SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator

Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents, potentially compromising data quality. To address this, machine learning-based shortened questionnaires have been developed. While these questionnaires possess high levels of accuracy, their practical use in clinical settings is hindered by a lack of transparency and the need for specialized machine learning expertise. This makes their integration into clinical workflows challenging and also decreases trust among healthcare professionals who prefer interpretable tools for decision-making. To preserve both predictive accuracy and interpretability, this study introduces the Symbolic Regression-Based Clinical Score Generator (SymScore). SymScore produces score tables for shortened questionnaires, which enable clinicians to estimate the results that reflect those of the original questionnaires. SymScore generates the score tables by optimally grouping responses, assigning weights based on predictive importance, imposing necessary constraints, and fitting models via symbolic regression. We compared SymScore's performance with the machine learning-based shortened questionnaires MCQI-6 (n=310) and SLEEPS (n=4257), both renowned for their high accuracy in assessing sleep disorders. SymScore's questionnaire demonstrated comparable performance (MAE = 10.73, R2 = 0.77) to that of the MCQI-6 (MAE = 9.94, R2 = 0.82) and achieved AUROC values of 0.85-0.91 for various sleep disorders, closely matching those of SLEEPS (0.88-0.94). By generating accurate and interpretable score tables, SymScore ensures that healthcare professionals can easily explain and trust its results without specialized machine learning knowledge. Thus, SymScore advances explainable AI for healthcare by offering a user-friendly and resource-efficient alternative to machine learning-based questionnaires, supporting improved patient outcomes and workflow efficiency.

Computerized Decision Support System and Fuzzy Logic Rules for Early Diagnosis of Pesticide-Induced Diseases

Many reflexologists employ outdated concepts that do not align with modern anatomy, physiology, and biophysics. Those concepts undermine physicians' confidence in their diagnosis. This study aims to improve the quality of medical care for workers in the agro-industrial complex who are exposed to pesticides by a fuzzy mathematical model using acupuncture points reflexes. Data obtained from reflex diagnostic methods are utilized in hybrid fuzzy decision rules to build a predictive classification model that integrates medical diagnosis with artificial intelligence. Pesticide exposure leads to cardiovascular and nervous system bronchopulmonary diseases, as well as kidney and liver tissue pathology. The developed model generates decision rules for early prediction of nervous system disorders, particularly when the primary risk factor is exposure to agricultural pesticides containing nitrates. In modern medical practice, there is a growing interest in ancient methods of reflex diagnostics and therapies based on maintaining the energy balance of an organism's meridian structures. However, the lack of a solid theoretical foundation explaining the mechanisms of interaction between internal and surface meridian structures poses a significant obstacle to wider adoption of reflex diagnostic techniques. This limitation severely hampers the potential of acupuncture. Moreover, many reflexologists in practice tend to overstate the benefits of acupuncture, which may lead to errors, that undermine the appropriate approach to diagnosis and treatment. The proposed model proves valuable for the healthcare of agro-industrial complex workers, as its decision-making process achieves an accuracy rate of over 85% in forecasting nervous system disorders.

Clinical implications of the latest advances in gastrointestinal tumor research

In this editorial, we provide commentary on six articles recently published in the World Journal of Gastrointestinal Oncology. These articles collectively present the latest findings in the field of gastric and colorectal cancer (CRC) research. The global incidence of gastric cancer varies based on geographical location, age, and sex. The disease predominantly affects middle-aged and elderly individuals, with a slightly higher prevalence in men than in women. CRC is characterized by a low 5-year survival rate and high mortality rate. It primarily affects individuals over the age of 50, and the risk of disease increases with age. Both gastric and CRC pose significant health threats, thus requiring more effective diagnostic, therapeutic, and supportive care strategies to improve patient outcomes. The articles discussed in this editorial encompass topics such as screening, diagnosis, mechanisms of progression, and postoperative recovery in gastric and CRC, and the findings offer valuable insights for clinical decision-making in the diagnosis, treatment, and prognosis of gastrointestinal cancers.

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review

Background

Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus.

Main Applications

The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas.

Challenges and Limitations

Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare.

Conclusion

The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

Integrating Artificial Intelligence in Pediatric Healthcare: Parental Perceptions and Ethical Implications

BACKGROUND

Our study aimed to explore the way artificial intelligence (AI) utilization is perceived in pediatric medicine, examining its acceptance among patients (in this case represented by their adult parents), and identify the challenges it presents in order to understand the factors influencing its adoption in clinical settings.

METHODS

A structured questionnaire was applied to caregivers (parents or grandparents) of children who presented in tertiary pediatric clinics.

RESULTS

The most significant differentiations were identified in relation to the level of education (e.g., aversion to AI involvement was 22.2% among those with postgraduate degrees, 43.9% among those with university degrees, and 54.5% among those who only completed high school). The greatest fear among respondents regarding the medical use of AI was related to the possibility of errors occurring (70.1%).

CONCLUSIONS

The general attitude toward the use of AI can be considered positive, provided that it remains human-supervised, and that the technology used is explained in detail by the physician. However, there were large differences among groups (mainly defined by education level) in the way AI is perceived and accepted.

ChatGPT utilization within the building blocks of the healthcare services: A mixed-methods study

Introduction

ChatGPT, as an AI tool, has been introduced in healthcare for various purposes. The objective of the study was to investigate the principal benefits of ChatGPT utilization in healthcare services and to identify potential domains for its expansion within the building blocks of the healthcare industry.

Methods

A comprehensive three-phase study was conducted employing mixed methods. The initial phase comprised a systematic review and thematic analysis of the data. In the subsequent phases, a questionnaire, developed based on the findings from the first phase, was distributed to a sample of eight experts. The objective was to prioritize the benefits and potential expansion domains of ChatGPT in healthcare building blocks, utilizing gray SWARA (Stepwise Weight Assessment Ratio Analysis) and gray MABAC (Multi-Attributive Border Approximation Area Comparison), respectively.

Results

The systematic review yielded 74 studies. A thematic analysis of the data from these studies identified 11 unique themes. In the second phase, employing the gray SWARA method, clinical decision-making (weight: 0.135), medical diagnosis (weight: 0.098), medical procedures (weight: 0.070), and patient-centered care (weight: 0.053) emerged as the most significant benefit of ChatGPT in the healthcare sector. Subsequently, it was determined that ChatGPT demonstrated the highest level of usefulness in the information and infrastructure, information and communication technologies blocks.

Conclusion

The study concluded that, despite the significant benefits of ChatGPT in the clinical domains of healthcare, it exhibits a more pronounced potential for growth within the informational domains of the healthcare industry's building blocks, rather than within the domains of intervention and clinical services.