Financial Aid Research Articles

A bibliometric analysis of the advance of artificial intelligence in medicine

This bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 to September 2023. Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others. 2,064 articles were analyzed, and a significant increase in global academic production has been evident in the last five years. International collaboration was notable, with China and the United States leading in knowledge contribution. The keyword analysis highlights the breadth of topics and applications of AI in medicine, with particular emphasis on cancer detection, dengue diagnosis, and medical image analysis, among others. In conclusion, this study highlights the growing academic interest in the application of AI in medicine and the need for collaborative research. The findings underscore the relevance of these technologies in key areas of health care, contributing significantly to advances in medical diagnosis and prognosis.

Boosting wisdom of the crowd for medical image annotation using training performance and task features.

A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI.

Europe and Arab States’ Migration-related Instruments with Africa: Dilemma of Soft Power and Conflictual Interests

The national and regional interests of the European Union, oil-rich Gulf Cooperation Council and Africa are conflictual in addressing international labour migration. This underscores the structure and ineffectiveness of bilateral migration instruments. The European Union canvasses for migrants’ returns, readmission and outsourcing of national border controls; Arab states show reluctance to stop the predominant employer–sponsorship system that curtails labour migrants’ human rights, while African states detest migrants’ return and readmission, and prefer liberalisation of legal migration and flow of remittances. This article is anchored on the hegemonic stability and regime frame of interventionist liberalism theoretical perspectives to highlight Europe’s soft power in migration partnerships or agreements deployed via financial aid and development projects as a way to drive the migration-development-security nexus with African countries. Essentially, African states’ asymmetric economic relationship with Europe has accentuated the carrot-and-stick approach, while Arab states’ slow pace in allowing the mobility of African labour migrants has led to a reduction in remittance flows. It is recommended that the dialogues between the regional blocs and countries leading to labour migration agreements must find a common ground of mutual interests for effective partnerships to emerge.

Enhancing College Access in Zimbabwe: Impact of Student Loans and Grants

This study explores how government interventions, grants, and loans influence college access in Zimbabwe, focusing on understanding the factors affecting college access and retention rates. The research aims to inform evidence-based decision-making regarding college access through a mixed research approach integrating qualitative and quantitative methods. Combining both qualitative and quantitative methods to ensure the validity and reliability of the findings. It analyses secondary data sources, such as literature, reports, and statistical datasets, to explore aspects like college access, enrollment trends, poverty rates, financial aid programs, and student loan systems in Zimbabwe. Triangulation techniques enhance the credibility of the study's findings by integrating evidence from various sources and employing diverse data collection and analysis methods, enriching perspectives and strengthening the validity and reliability of the study's conclusions. Key findings suggest that grants and loans significantly contribute to increasing college enrollment rates, particularly among economically disadvantaged groups. However, enrollment disparities persist, especially among marginalized populations like women. Despite a modest increase in government spending on education, concerns arise due to a slowdown in overall enrollment growth. Barriers to student loan uptake include complex application processes, concerns about debt burdens, and uncertainties regarding post-graduation employment prospects. The study emphasizes the importance of comprehensive policy interventions to simplify eligibility criteria, enhance financial literacy, and promote employment pathways. Using human capital and open systems theories, the research lays a groundwork for evidence-based decision-making to enhance access to higher education in Zimbabwe.

Oropharyngeal Cancer Staging Health Record Extraction Using Artificial Intelligence

Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intelligence in medicine, data abstraction may be associated with reduced costs but increased efficiency and accuracy of cancer staging. To evaluate an algorithm using an artificial intelligence engine capable of extracting essential information from medical records of patients with oropharyngeal cancer and assigning tumor, nodal, and metastatic stages according to American Joint Committee on Cancer eighth edition guidelines. This retrospective diagnostic study was conducted among a convenience sample of 806 patients with oropharyngeal squamous cell carcinoma. Medical records of patients with staged oropharyngeal squamous cell carcinomas who presented to a single tertiary care center between January 1, 2010, and August 1, 2020, were reviewed. A ground truth cancer stage dataset and comprehensive staging rule book consisting of 135 rules encompassing p16 status, tumor, and nodal and metastatic stage were developed. Subsequently, 4 distinct models were trained: model T (entity relationship extraction) for anatomical location and invasion state, model S (numerical extraction) for lesion size, model M (sequential classification) for metastasis detection, and a p16 model for p16 status. For validation, results were compared against ground truth established by expert reviewers, and accuracy was reported. Data were analyzed from March to November 2023. The accuracy of algorithm cancer stages was compared with ground truth. Among 806 patients with oropharyngeal cancer (mean [SD] age, 63.6 [10.6] years; 651 males [80.8%]), 421 patients (52.2%) were positive for human papillomavirus. The artificial intelligence engine achieved accuracies of 55.9% (95% CI, 52.5%-59.3%) for tumor, 56.0% (95% CI, 52.5%-59.4%) for nodal, and 87.6% (95% CI, 85.1%-89.7%) for metastatic stages and 92.1% (95% CI, 88.5%-94.6%) for p16 status. Differentiation between localized (stages 1-2) and advanced (stages 3-4) cancers achieved 80.7% (95% CI, 77.8%-83.2%) accuracy. This study found that tumor and nodal staging accuracies were fair to good and excellent for metastatic stage and p16 status, with clinical relevance in assigning optimal treatment and reducing toxic effect exposures. Further model refinement and external validation with electronic health records at different institutions are necessary to improve algorithm accuracy and clinical applicability.

Graph Artificial Intelligence in Medicine.

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way toward clinically meaningful predictions.

Agrarian Distress in Indian Punjab: A Public Policy Paralysis

The drift towards resource-intensive, high-yield crop-based cultivation in the early 1960s, widely called the Green Revolution, has led to ecologically unviable cropping patterns with adverse monetary fallouts in Punjab, which had once earned the sobriquet, the Granary of India. Since price and availability are the basis of competition, farmers are not sufficiently incentivised to address the vital issue of the decline in natural resources. As a result, securing food and nutrition while conserving the environment is challenging. So is mobilising diverse and apposite stakeholders whose perspective is vital to move ahead sustainably. The vital issue is considering the likely impact of input incentives, output support or restraints, technical backing, and financial aid on agricultural outcomes that vary across contexts and crops. This decision-based case introduces the participants to real-life socio-economic and ecological challenges, which they can relate with any other agriculture-based economy. The highlight of the case is the way the problem is anticipated to be viewed by different stakeholder groups and the conflicting policy options supporting conventional vis-à-vis organic farming. It is also interesting to review the conditions under which the proposed solution is most robust. Such a review would likely sensitise participants to how a change in perspectives can impact the suitability of alternative policy options.

Teaching artificial intelligence in medicine

Teaching artificial intelligence in medicine

Impact of COVID-19 on Profession and Health of Female Sex Workers: Apprehension, Acquiescence, and Resilience.

In Surat city of Gujarat, India, female sex workers (FSW)-based targeted interventions (TI) and community-based organizations (CBO) have been functioning since 1998. To document the impact of the COVID-19 lockdown (March-May 2020) on FSWs and STI/RTI/HIV preventive measures provided through TI/CBOs in Surat city, sequential, explanatory mixed-methods design was used. We conducted a desk review of quarterly programmatic data (2018-2020) of four TIs and CBOs and interviewed of 221 FSWs to study pre- and post-lockdown socioeconomic conditions, engagement in sex work, health services during COVID-19 lockdown and coping strategies, followed by five focus group discussions of FSWs and TI/CBO project staff. Study identified four major themes: reasons for working as a FSW during COVID-19 lockdown, entry to sex work during or as a consequence of lockdown, exploitation of FSWs during lockdown, and challenges of TI and CBOs in service provision in pandemic situation. Number of new registrations and STI/RTI detection increased immediately after lockdown. TI/CBO and other NGOs working for women welfare must build financial independence by training them in microeconomics, savings, and entrepreneurship. TI/CBO staff must also include experts in finance or business, trained in emergency response. The presence of strong TI/CBO network facilitated prompt delivery of essential commodities and financial aid to FSWs. However, laboratory and screening services were hampered, which warrants need of introducing self-test kits. In backdrop of decrease in condom distribution, continuation of sex work during lockdown and disruption of routine HIV/STI/RTI screening facilities, our study also recommends intensified HIV/STI/RTI screening among FSWs.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

Addressing the Housing Crisis: Insights from a Study on University Students in Turkey

Housing stands as a fundamental human right, acknowledged by numerous international bodies and statutes. Despite this recognition, only a handful of nations ensure adequate housing for their populace. This research endeavors to forge a connection between housing rights and social policy through a comprehensive literature review. Drawing from data provided by the Turkish Statistical Institute and the Ministry of Youth and Sports, the study addresses the escalating housing concerns among university students in Turkey. With an increasing student population and insufficient accommodation facilities, the issue has reached a critical juncture. Most students are compelled to reside in state dormitories, which lack the capacity to meet demand. Moreover, economic hardships and a dearth of financial aid exacerbate the housing crisis. The findings underscore the urgent need for the development of policies and solutions tailored to address the housing predicament faced by university students in Turkey.

Paying Them to Hate US: The Effect Of U.S. Military aid on Anti-American Terrorism, 1968-2018

Abstract How does anti-American terrorism in recipient countries respond to U.S. military aid? Does aid protect U.S. interests abroad or does it have unintended consequences for U.S. security? To answer these questions, we estimate the effect of U.S. military aid on anti-American terrorism in recipient countries for a sample of 174 countries between 1968 and 2018. We find that higher levels of aid especially for military financing and education are associated with a higher likelihood of anti-American terrorism in aid-receiving countries. Examining potential transmission channels, we show that more U.S. military aid correlates with lower military capacity and increases in corruption and exclusionary policies in recipient countries. Our findings are consistent with the argument that military aid aggravates local grievances, creating anti-American resentment and leading to anti-American terrorism. Indeed, we also provide tentative evidence that U.S. military aid is associated with lower public opinion about the United States in recipient countries.

Transforming Escalation to Negotiation: Rethinking Confidence-Building Measures (CBMs) in the Ongoing Russia-Ukraine War

There have been several pieces of literature on the current war between Russia and Ukraine but little emphasis has been placed on transforming the growing escalation into negotiation. In this study, I discuss the causalities that surround the current war between the Russian Federation and Ukraine from different prisms. Various justifications and other indices which provide the likelihood of escalation to a nuclear conflict were touched. Also, I assessed, if at all, there was politics behind the supplies of ‘defensive weapons’ and other military aid to the warring parties. I propose an urgent arms control treaty not only between the United States and Russia but should also include China as the emerged third force, and other minor nuclear powers. Although, the Confidence Building Measures (CBMs) are mostly prominent before a complex conflict is transformed into conventional war, thus, this study suggests the roles the CBMs can still play in the current war. As such, the study relies on relevant written sources which make the study qualitative in nature.

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BackgroundThe current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.MethodsThis study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.ResultsFrom a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.ConclusionMedical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Exploring Service Accessibility and Quality for Differently-Abled Students: A Qualitative Analysis at a South African University of Technology

This qualitative study investigates the service experiences of differently-abled students within the Financial Aid Department of a prominent South African University of Technology. Employing a semi-structured interview schedule aligned with the Service Quality model (SERVQUAL), five dimensions—tangibles, assurance, empathy, reliability, and responsiveness—are explored. The study unveils the significant challenges students face, including issues related to the location and accessibility of the department, staff approachability, and the reliability of communication systems. The discussion section draws on relevant literature to synthesise the findings, providing comprehensive insights into the intricacies of service delivery for differently-abled students. The study concludes with actionable recommendations for enhancing service quality and inclusivity, addressing the unique needs of this student demographic in the evolving landscape of South African higher education.

Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms.

The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973-0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson's coefficient of 0.876 (95% CI 0.859-0.891; p < 0.001). The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.

Ensemble learning for retinal disease recognition under limited resources.

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of retinal OCT images often presents challenges stemming from privacy concerns and the resource-intensive labeling procedures, which contradicts the prevailing notion that DL models necessitate substantial data volumes for achieving superior performance. Moreover, limitations in available computational resources constrain the progress of high-performance medical artificial intelligence, particularly in less developed regions and countries. This paper introduces a novel ensemble learning mechanism designed for recognizing retinal diseases under limited resources (e.g., data, computation). The mechanism leverages insights from multiple pre-trained models, facilitating the transfer and adaptation of their knowledge to retinal OCT images. This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch. Comprehensive experimentation on real-world datasets demonstrates that the ensemble models constructed by the proposed ensemble method show superior performance over the baseline models under sparse labeled data, especially the triple ensemble model, which achieves the accuracy of 92.06%, which is 8.27%, 7.99%, and 11.14% better than the three baseline models, respectively. In addition, compared with the three baseline models learned from scratch, the triple ensemble model has fewer trainable parameters, only 3.677M, which is lower than the three baseline models of 8.013M, 4.302M, and 20.158M, respectively.

Artificial intelligence in medicine

Artificial intelligence in medicine

Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study.

The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system. A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05). A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system. This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.

AI IN AGRICULTURE:A LOOMING CHALLENGE, A GLEAMING OPPORTUNITY

As the global population burgeons, reaching an estimated 10 billion by 2050, the agricultural sector faces immense pressure to enhance productivity and sustainability Facing these challenges, the incorporation of artificial intelligence (AI) shows great potential for transforming farming methods This comprehensive review explores the multifaceted role of AI in agriculture, highlighting its opportunities, challenges, and pathways towards building resilient and sustainable food systems. From precision farming and crop health monitoring to predictive analytics and agricultural robotics, AI offers transformative solutions to address pressing agricultural challenges. Yet, integrating AI into agriculture faces challenges like accessibility, data privacy, and skills training. Collaborative efforts and strategic interventions can overcome these hurdles, unleashing AI's potential to secure food, combat climate change, and foster inclusive agricultural growth.