AI-based Tools Research Articles

Enhancing Conversations in Migrant Counseling Services: Designing for Trustworthy Human-AI Collaboration

Migrants often face a complex situation for navigating services and administrative processes to transition and integrate into a new environment. They rely on guidance from service advisors and informal peers like other migrants to assist them. Service advisors try to understand, explore, and resolve migrant concerns through conversational interaction, while often seeking advice from other domain experts. The emergence of artificial intelligence (AI)-mediated communication holds the potential for enhancing communication between migrants and service advisors while leveraging the expertise of service advisors and facilitating migrants' autonomy in the integration process. Our research investigates the social context, mental models, and challenges encountered in guidance counseling for migrants in Finland, while examining what role AI-mediated communication can play in supporting the process. Based on our findings, we propose design implications for improving shared understanding between service advisors and migrants through salient summarization of conversations and creating collaborative visual narratives. Our findings also indicate the crucial role of leveraging peer-based knowledge between (and within) service advisors, domain experts, and migrants, which can be facilitated by AI-based tools. Our exploratory research offers insights into human-centered AI integration in the migration process, as well as the opportunities and challenges of using AI-mediated communication to improve the migrant experience.

The Convergence of Artificial Intelligence and Digital Marketing

Indonesia has witnessed significant growth in internet penetration, with over 212 million users connected online. This digital boom has been driven by e-commerce, which accounted for 52% of the country's gross merchandise value in 2024, reaching a staggering 1,155 trillion rupiah. To sustain this momentum, e commerce players are adopting digital marketing strategies. The integration of artificial intelligence and big data is being explored to enhance consumer services. This study examines the strategies for integrating artificial intelligence in digital marketing. Using a qualitative descriptive approach based on literature review, our research identifies three key strategies that manufacturers can employ to improve marketing services and provide a seamless shopping experience: (1) leveraging AI-based social media analysis tools; (2) optimizing Search Engine Optimization; and (3) utilizing AI-powered chatbots.

Artificial intelligence applications in ophthalmic surgery.

Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applications such as diagnostic imaging. Given the proliferation of artificial intelligence (AI)-technologies created for video-based imaging, ophthalmic microsurgery is likely to experience significant benefits from the application of emerging technologies to multiple facets of the care of the surgical patient. Proof-of-concept research and early phase clinical trials are in progress for AI-based surgical technologies that aim to provide preoperative planning and decision support, intraoperative image enhancement, surgical guidance, surgical decision-making support, tactical assistive technologies, enhanced surgical training and assessment of trainee progress, and semi-autonomous tool control or autonomous elements of surgical procedures. The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into our microsurgical paradigm.

Artificial intelligence for language learning and teaching: A narrative literature study

The purpose of this study was to examine teachers' beliefs, the factors that influence the use of AI tools, and the advantages, disadvantages, opportunities, and threats of artificial intelligence in language learning and teaching. To accomplish this, the study used a narrative literature review and ATLAS.ti 9 to analyze the data. The results of the study showed that teachers believed that Artificial Intelligence in Language Learning and Teaching (AILLT) included assessing student needs, developing appropriate learning resources, promoting collaboration among resources, providing real-time assessment tools to improve the educational process, and increasing teaching efficiency and effectiveness. Second, AI tools for writing tools, including Paperpal, Quillbot, Jenni AI, ChatGPT, Elicit, as well as in applications to improve speaking skills, such as Speeko and Vocaroo, and "Siri and Say It" for listening and pronunciation skills. Third, factors that may influence students' use of AI-based language learning tools include adaptability, engagement, motivation, autonomy, immediate or direct feedback, accessibility and inclusivity, and teacher and parent support. This research found that AI enhances accessibility, adaptability, personalization, and immediate feedback, but it also has limitations, such as dependence on technology, inadequate human interaction, limited contextual understanding, and algorithmic biases. In addition, AILLT faces potential threats such as quality control, privacy concerns, and job or task displacement. Narrative literature studies provide theoretical insights, and it is expected that future studies at different levels of language learning will incorporate empirical evidence from experimental and case study-based research.

Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients

Background: Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses data from Korean cancer patients. Methods: A retrospective analysis of 1200 Korean cancer patients treated with radiotherapy was conducted. Auto-contours generated via OncoStudio and Protégé AI were compared with manual contours across the head and neck and thoracic, abdominal, and pelvic organs. Accuracy was assessed using the Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD). Feedback was obtained from 10 participants, including radiation oncologists, residents, and radiation therapists, via an online survey with a Turing test component. Results: OncoStudio outperformed Protégé AI in 85% of the evaluated OARs (p < 0.001). For head and neck organs, OncoStudio achieved a similar DSC (0.70 vs. 0.70, p = 0.637) but significantly lower MSD and 95% HD values (p < 0.001). In thoracic organs, OncoStudio performed excellently in 90% of cases, with a significantly greater DSC (male: 0.87 vs. 0.82, p < 0.001; female: 0.95 vs. 0.87, p < 0.001). OncoStudio also demonstrated superior accuracy in abdominal (DSC 0.88 vs. 0.81, p < 0.001) and pelvic organs (male: DSC 0.95 vs. 0.85, p < 0.001; female: DSC 0.82 vs. 0.73, p < 0.001). Clinicians favored OncoStudio in 70% of cases, with 90% endorsing its clinical suitability for Korean patients. Conclusions: OncoStudio, which is tailored for Korean patients, demonstrated superior segmentation accuracy across multiple anatomical regions, suggesting its suitability for radiotherapy planning in this population.

PragFormer: Data-Driven Parallel Source Code Classification with Transformers

AbstractMulti-core shared memory architectures have become ubiquitous in computing hardware nowadays. As a result, there is a growing need to fully utilize these architectures by introducing appropriate parallelization schemes, such as OpenMP worksharing-loop constructs, to applications. However, most developers find introducing OpenMP directives to their code hard due to pervasive pitfalls in managing parallel shared memory. To assist developers in this process, many compilers, as well as source-to-source (S2S) translation tools, have been developed over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. Recently, many data-driven AI-based code completion (CC) tools, such as GitHub CoPilot, have been developed to ease and improve programming productivity. Leveraging the insights from existing AI-based programming-assistance tools, this work presents a novel AI model that can serve as a parallel-programming assistant. Specifically, our model, named PragFormer, is tasked with identifying loops that can benefit from conversion to parallel worksharing-loop construct (OpenMP directive) and even predict the need for specific data-sharing attributes clauses on the fly. We created a unique database, named Open-OMP, specifically for this goal. Open-OMP contains over 32,000 unique code snippets from different domains, half of which contain OpenMP directives, while the other half do not. We experimented with different model design parameters for these tasks and showed that our best-performing model outperforms a statistically-trained baseline as well as a state-of-the-art S2S compiler. In fact, it even outperforms the popular generative AI model of ChatGPT. In the spirit of advancing research on this topic, we have already released source code for PragFormer as well as Open-OMP dataset to public. Moreover, an interactive demo of our tool, as well as a Hugging Face webpage to experiment with our tool, are already available.

Impact of artificial intelligence on healthcare-associated infection control: a systematic review

Abstract Background Healthcare-associated infections (HAIs) represent a significant public health concern, correlating with increased morbidity, mortality rates and healthcare expenditures. While artificial intelligence (AI) systems offer transformative potential in enhancing HAIs detection and control practices, the actual performance effectiveness of these systems remains uncertain. This systematic review updates a previously published study from 2020 and evaluates the performance of AI-based tools for surveillance, detection, and control of HAIs. Methods PRISMA 2020 guidelines were applied. The study protocol has been registered in PROSPERO (ID: CRD42024513145). PubMed, Embase, Scopus and Web of Science were searched for experimental and observational studies assessing the performance of AI-based tools to detect and control HAIs, published in English. Results From 8,701 articles initially identified, 4,212 records were removed due to duplication. Out of 4,489 papers screened, 147 were included. Studies reported performance measures including sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, accuracy, precision, F1 score. Significant heterogeneity was found in the types of technology, infections targeted, health care settings and data sources between studies. Conclusions The increase in published evidence since the previous review reflects the growing interest and use of new technologies such as Large Language Models, showing promising performance in surveillance, early diagnosis and prediction of HAIs. However, the observed heterogeneity in study designs, targeted infections, healthcare settings, and data sources underscores the need for standardised methodologies and robust validation processes to ensure the reliability and comparability of results across different studies. Key messages • The use of AI-based tools has the potential to enhance surveillance, detection, and control of healthcare-associated infections, offering a transformative impact on healthcare systems. • Standardised methodologies and validation processes are needed to ensure comparable results across studies and to maximise the real-world impact of AI tools in HAI surveillance and control efforts.

Examining generative AI–mediated informal digital learning of English practices with social cognitive theory: a mixed-methods study

Abstract This study explores the integration of generative artificial intelligence (GenAI) in informal digital learning of English (IDLE) practices, focusing on its potential to enhance language learning outcomes and addressing the technological challenges language teachers face in utilising AI-based tools to facilitate second language acquisition. Based on the research context of IDLE and holistic learning ecology and drawing on the theoretical frameworks of technological pedagogical and content knowledge and social cognitive theory, we performed a mixed-methods investigation with an empirical experiment to assess the effectiveness of GenAI followed by semi-structured interviews. The results suggest that the GenAI-mediated IDLE practices effectively improve college students’ oral proficiency in English from both technological and humanistic perspectives. However, results also indicate that the GenAI conversational partner alone is not adequate to provoke continuous extramural GenAI-mediated IDLE practices. We discuss the theoretical and pragmatic significance of GenAI-mediated IDLE in educational equity and reformation.

Recent Progress and Current Status of Artificial Intelligence in Skin Cancer Diagnosis: A Systematic Review—Where do we Stand?

Introduction: Skin cancer is one of the most prevalent forms worldwide, with a significant increase in recent decades. Real-time and accurate detection can reduce the burdens of invasive treatments. The advent of Artificial Intelligence (AI) and Machine learning (ML) has introduced multiple tools to aid accurate and early detection, categorizing dermatological images and proving especially valuable in regions with a shortage of specialists. However, the adoption of these AI-based tools requires consideration of efficacy, safety, and ethical implications. Objective: The systematic review aims to evaluate existing research on the detection, categorization, and assessment of skin cancer images. Methods: The systematic literature review is conducted based on studies published from 2018 to 2023 in PubMed, Scopus, Embase, Web of Science, IEEE Xplore, ACM DL, and Ovid MEDLINE. Study selection, data extraction, and inclusion are carried out after a proper evaluation of the studies. Results are presented in tables and figures using a narrative synthesis. Results: The search identified 687 studies from the database. However, after three phases of identification, screening, and evaluation, only 16 studies were chosen, focusing on developing and validating AI tools to detect, diagnose, and categorize skin cancer. This systematic review covers the selected studies in multiple dimensions. Conclusion: The use of AI and ML in dermatology has revolutionized the early detection of cancer, but it is necessary to validate and collaborate with healthcare professionals to ensure efficacy, safety, and effectiveness.

"SWALLOWING AND ASPIRATION: HOW TO EVALUATE AND TREAT SWALLOWING DISORDERS ASSOCIATED WITH ASPIRATION PNEUMONIA IN OLDER PERSONS".

BACKGROUND Aspiration pneumonia (AP) is the most severe complication of oropharyngeal dysphagia (OD). It is highly underdiagnosed and undertreated among older patients hospitalized with community-acquired pneumonia (CAP). Our aim is to review the state of the art in the diagnosis and treatment of swallowing disorders associated with AP. METHODOLOGY We performed a narrative review, including our experience with prior studies at Hospital de Mataró, on the diagnosis and treatment of AP. RESULTS AP refers to pneumonia occurring in patients with swallowing disorders, frequently coinciding with poor oral health and vulnerability. Its main risk factors include oropharyngeal aspiration, impaired health status, malnutrition, frailty, immune dysfunction and oral colonization by respiratory pathogens. Incidence is estimated at between 5%-15% of cases of CAP but it is highly underdiagnosed. Diagnostic criteria for AP have not been standardized but should include its main pathophysiological element, oropharyngeal aspiration. Recently, a clinical algorithm was proposed, based on the recommendations of the Japanese Respiratory Society (JRS), that includes aspiration risk factors and clinical evaluation of OD. To facilitate the task for healthcare professionals, new AI-based screening tools for OD combined with validated clinical methods such as the volume-viscosity swallowing test (V-VST) for the detection of AP are being validated. Prevention and treatment of AP require multimodal interventions aimed to cover the main risk factors: textural adaptation of fluids and diets to avoid oropharyngeal aspiration; nutritional support to avoid malnutrition; and oral hygiene to reduce oral bacterial load. CONCLUSIONS The diagnosis of AP must be based on standardized criteria providing evidence on the main etiological factor, oropharyngeal aspiration. Clinical algorithms are valid in the diagnosis of AP and the identification of its main risk factors. Combination of AI-based tools with V-VST can lead to massive screening of OD and save resources and improve efficiency in the detection AP.

An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept

Efficient design, production, and optimization of new safe and sustainable by design materials for various industrial sectors is an on-going challenge for our society, poised to escalate in the future. Wood-based composite materials offer an attractive sustainable alternative to high impact materials such as glass and polymers and have been the focus of experimental research and development for years. Computational and AI-based materials design provides significant speed-up the development of these materials compared to traditional methods of development. However, reliable numerical models are essential for achieving this goal. The AI-TranspWood project, recently funded by the European Commission, has the ambition to develop such computational and AI-based tools in the context of transparent wood (TW), a promising composite with potential applications in various industrial fields. In this project we advance the development specifically by using an Artificial Intelligence (AI)-driven multiscale methodology.

Formative assessment in artificial integrated instruction: delving into the effects on reading comprehension progress, online academic enjoyment, personal best goals, and academic mindfulness

This paper investigates the effects of formative assessment (FA) via artificial intelligence (AI) on EFL students’ reading comprehension development, online academic enjoyment, personal best goals (PBGs), and academic mindfulness. To achieve this, 80 Kuwaiti students were selected through convenience sampling and divided into the experimental group (EG) and the control group (CG). Pretests were conducted to assess the initial levels of the dependent variables. The EG received AI-based instruction through the Nearpod platform, while the CG received traditional face-to-face instruction. Both groups were taught ten reading passages, and post-tests were administered to measure reading comprehension progress, online academic enjoyment, PBGs, and academic mindfulness. ANCOVA test results revealed significant differences between the post-tests of the EG and CG, with the EG showing superior performance across all measures. These findings suggest that AI-based formative assessments can substantially enhance students’ academic and psychological outcomes, providing valuable insights for EFL instructors and material designers. Future research should explore the long-term effects of AI-based learning tools in diverse educational settings.

A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean percentile Hausdorff distance (95HD) of . Whereas a mean 95HD of was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, Jaccard Index acquired from our pipeline, while was stated in their paper.

The AI-empowered Researcher: Using AI-based Tools for Success in Ph.D. Programs

Generative artificial intelligence (AI) changes the picture of graduate education by providing personalized learning, automated feedback, intelligent research assistants, and automated content creation (George, 2023). AI tools will support doctoral students in text generation, language translation, responding to academic queries, and data collection and analysis and encourage self-learning and thinking development (Rasul et al., 2023; Zou & Huang, 2023). They also would be helpful for doctoral students working as teaching assistants and aiding in daily problems (Can et al., 2023; Parker et al., 2024). However, the rise of AI tools also leads to considerations of academic integrity, over-reliance on AI, misinformation, and the potential biases embedded in algorithms (George, 2023; Rasul et al., 2023). Echoing the opportunities and challenges of AI applications in research and learning, the ALISE Doctoral Students SIG wants to encourage a discussion on how doctoral students can use AI tools to empower us in the Ph.D. journey. The panel invites a diverse group of doctoral students/candidates to share how AI tools can facilitate data collection and analysis and their critical understanding of AI systems. Manar Alsaid will talk about using AI and machine learning to detect complex misinformation on social media. The talk aims to enhance our understanding of misinformation and reduce its negative impacts. This presentation will provide valuable insights for research on misinformation and information literacy. Adam Eric Berkowitz will introduce the black-box tinkering method that experimentally discerns how AI systems operate. The method enhances the transparency of AI systems, challenging the technocratic paradigm. With three examples, Berkowitz encourages attendees to learn what black-box tinkering is, how to identify cases using it, and potential opportunities to incorporate it in research. Anisah Herdiyanti will share insights from a study comparing transcripts generated by Otter.ai and Zoom Meetings. The presentation will highlight both the benefits and challenges of AI-based notes and transcription software, including technical concerns and the convenience of automated result delivery. The audience will enhance their understanding of AI tools in qualitative data transcribing and the ethical considerations in the process. Rebecca Bryant Penrose will showcase the use of HeyGen, an AI-based video generator and translation tool, in an international interview project between students at California State University Bakersfield and a Ukrainian artist/author. The presentation will increase awareness of the potential use of AI-based video and help researchers overcome language barriers in data collection. The panel will last 90 minutes, including a 5-minute introduction and a 5-minute wrap-up. Each panelist will have 10 minutes to present their topics, followed by 5-minute Q&As. A 25-minute moderated roundtable discussion will follow the panelists’ presentations to explore the potential use of different AI tools in research, including ChatGPT and AI-powered article summarizers. The panel’s learning outcomes include (1) Identifying challenges and opportunities to incorporate AI tools in research and study and (2) Explaining how to interact with AI tools to improve efficiency in research. It also provides a platform for doctoral students to share their knowledge of how AI changes research approaches and networks with each other.

The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO).

Recently, the availability of online medical resources for radiation oncologists and trainees has significantly expanded, alongside the development of numerous artificial intelligence (AI)-based tools. This review evaluates the impact of web-based clinical decision-making tools in the clinical practice of radiation oncology. We searched databases, including PubMed, EMBASE, and Scopus, using keywords related to web-based clinical decision-making tools and radiation oncology, adhering to PRISMA guidelines. Out of 2161 identified manuscripts, 70 were ultimately included in our study. These papers all supported the evidence that web-based tools can be transversally integrated into multiple radiation oncology fields, with online applications available for dose and clinical calculations, staging and other multipurpose intents. Specifically, the possible benefit of web-based nomograms for educational purposes was investigated in 35 of the evaluated manuscripts. As regards to the applications of digital and AI-based tools to treatment planning, diagnosis, treatment strategy selection and follow-up adoption, a total of 35 articles were selected. More specifically, 19 articles investigated the role of these tools in heterogeneous cancer types, while nine and seven articles were related to breast and head & neck cancers, respectively. Our analysis suggests that employing web-based and AI tools offers promising potential to enhance the personalization of cancer treatment.

AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language Processing

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes.

Trustworthy AI for semiconductor manufacturing

Trustworthy AI for semiconductor manufacturing Marcel van Gerven, a Professor of Artificial Cognitive Systems at Radboud University, discusses how his team’s research on AI models, algorithms, and demonstrators could serve as a blueprint for the semiconductor manufacturing industry. Artificial Intelligence (AI) is a key enabling technology that, when used responsibly, can bring about significant positive societal impact. One of the initiatives to foster this positive impact is the Dutch ROBUST ‘Trustworthy AI-based Systems for Sustainable Growth’ program. This ten-year Long Term Program (LTP) is a human- centered research program in AI that brings together knowledge institutes, industry, governmental organizations, and societal partners to develop AI-based methods and tools designed to create social impact and promote sustainable growth (Figure 1).

Advancing against drug-resistant tuberculosis: an extensive review, novel strategies and patent landscape.

Drug-resistant tuberculosis (DR-TB) represents a pressing global health issue, leading to heightened morbidity and mortality. Despite extensive research efforts, the escalation of DR-TB cases underscores the urgent need for enhanced prevention, diagnosis, and treatment strategies. This review delves deep into the molecular and genetic origins of different types of DR-TB, highlighting recent breakthroughs in detection and diagnosis, including Rapid Diagnostic Tests like Xpert Ultra, Whole Genome Sequencing, and AI-based tools along with latest viewpoints on diagnosis and treatment of DR-TB utilizing newer and repurposed drug molecules. Special emphasis is given to the pivotal role of novel drugs and discusses updated treatment regimens endorsed by governing bodies, alongside innovative personalized drug-delivery systems such as nano-carriers, along with an analysis of relevant patents in this area. All the compiled information highlights the inherent challenges of current DR-TB treatments, discussing their complexity, potential side effects, and the socioeconomic strain they impose, particularly in under-resourced regions, emphasizing the cost-effective and accessible solutions. By offering insights, this review aims to serve as a compass for researchers, healthcare practitioners, and policymakers, emphasizing the critical need for ongoing R&D to improve treatments and broaden access to crucial TB interventions.

AI Methods and Algorithms for Diagnosis of Intestinal Parasites: Applications, Challenges and Future Opportunities

Artificial intelligence (AI) transforms intestinal parasite diagnosis, particularly through deep learning models like convolutional neural networks (CNNs). This paper reviews the application of AI, especially CNNs, in automating parasite detection and classification from microscopic images. Integrating AI into parasitology diagnostics speeds up the process, reduces human error, and enhances treatment and patient outcomes. However, there is a need for more datasets reflecting the African context to ensure accurate ground-truthing, particularly in low- and middle-income countries (LMICs) across Africa. Most AI models for medical diagnosis are trained on datasets from high-income countries, which may not capture the unique epidemiological, genetic, and environmental factors prevalent in African populations. This can lead to less accurate diagnoses and treatment recommendations in African LMICs. For example, intestinal parasitic infections are common in many African regions, yet the datasets used to train AI models often lack sufficient representation from these areas. Developing datasets that reflect the diverse African context is crucial for improving the accuracy and reliability of AI-based diagnostic tools. Issues like overfitting, data privacy, and cost also require attention. Collaboration between researchers, healthcare professionals, and technologists is essential to address these challenges. Standardized protocols for data collection, model training, and validation are necessary for reliable AI systems. Combining AI with traditional techniques holds promise for better parasite diagnosis, ultimately improving African healthcare outcomes

ECG data analysis to determine ST-segment elevation myocardial infarction and infarction territory type: an integrative approach of artificial intelligence and clinical guidelines.

Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm. Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI). In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease. These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.