OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

Academic publishing is at a critical juncture. The challenges faced by the academics are mired in controversy. Among theseare three hotly debated concerns. First is the issue of whether technological innovations such as artificial intelligence (AI)improves research efficiency or if its use sacrifices research integrity.Another is the controversy between paywall publishingand open access. Lastly, adapting an appropriate business model for sustainability is a contentious issue and the choice betweena commercial or a university-based publishing platform is a difficult one.

Traditional models of scientific investigation relied on tedious intellectual calisthenics in all aspects of research —identifying research gaps, reviewing of published literature, devising valid methodology, collecting data, analysing results, and,finally, drawing conclusions. With the advent of powerful tools employing artificial intelligence, these heavy tasks are efficientlycarried out. The dilemma lies in determining which parts of the work can be attributed to the authors and which are ascribedto the output of large language models (LLMs) and other automated assistance employed.Despite requiring adequate vettingby experts of these AI-aided output, many in the scientific community still question these methods. Can research employingAI be considered honest work? Will full disclosure answer doubts as to the integrity of the scientific work?

Indeed, LLMs just gather information that is already out there, albeit more efficiently. After all, science progresses bystanding on the shoulder of giants. AI makes such work comprehensive and efficient. Standing on those proverbial shoulders,however, require access to prior work, hence our next challenge in academic publishing--open access versus paid access.Paywalls limit the benefits of valuable research to institutions and universities with the capacity to pay. Excluded from these arethose from low resourced countries, with nations from the global south being affected disproportionately. Additionally, whilenumerous authors appreciate the features of open access as it improves their impact and visibility, many feel unduly burdenedsince the cost of publishing in this format is passed on to them.

This brings us to our third issue: who bears the cost of academic publishing? Indeed, it is a lucrative industry, generatingan annual revenue of US$19 billion and an estimated 40 percent profit margin. Many, however, find fault in this businessmodel as concerns about the profit motives of the commercial publishers far overshadow their sustainability goals.

How do we navigate this landscape of controversies? We, at the ACTA, as part of the community of scholars, would needto clarify our mission. Our goals for this publication should be consistent with our values. These values, such as scientific rigor,integrity, and accountability, should be reflected in our policies. We should be cognizant of the role we play in national scientificdiscourse while we endeavor to make an impact in the global scene. We are accountable to our stakeholders — nurturingearly career scholars, supplying evidence to health policymakers, and being accountable to those who provide resources tosustain us. This stewardship is essential so that ACTA will stand shoulder to shoulder with the giants on which science buildsupon to benefit future generations.

Artificial Intelligence ; Commerce ; Costs And Cost Analysis ; Disclosure ; Drawing ; Efficiency ; Family Characteristics ; Forecasting ; Goals ; Gymnastics ; Health ; Health Resources ; Industry ; Intelligence ; Inventions ; Language ; Literature ; Methods ; Play And Playthings ; Policy ; Publications ; Publishing ; Research ; Residence Characteristics ; Role ; Science ; Shoulder ; Social Responsibility ; Universities ; Ursidae ; Volition ; Work ; World Health Organization

The growing use of generative artificial intelligence (AI) has coincided with more timely manuscript submissions and improved efficiency in scientific writing. While AI tools help authors produce clear and well-structured work, excessive or undisclosed reliance raises concerns about originality, authenticity, and the integrity of scholarly publications. Editors increasingly recognize AI-assisted writing and face the challenge of preserving rigorous standards. Emerging guidelines emphasize transparency in AI use, alongside the need to retain individual voice and diversity in scientific expression. This editorial highlights the balance between leveraging AI’s benefits and safeguarding ethical publication practices, while acknowledging contributors to the current issue.

Writing ; Work ; Voice ; Reference Standards ; Artificial Intelligence ; Intelligence ; Efficiency

Artificial intelligence (AI) and generative artificial intelligence (GenAI) have gained increasing relevance in occupational therapy (OT) due to their potential to enhance clinical practice, optimize client care, and shape the future of OT education. Despite growing international evidence, literature addressing AI use in OT remains limited in the Philippine context. This Special Collection on AI in Occupational Therapy seeks to address this gap by examining the perspectives and experiences of key stakeholders across OT education and practice through a stakeholder-informed qualitative approach. Using interviews and focus group discussions, insights from school administrators, OT educators, interns, students, and clinicians are gathered to explore their attitudes, concerns, and lived experiences related to AI use in occupational therapy. These multi-perspective findings aim to inform the development of contextually grounded frameworks, institutional policies, and evidence-based programs that support ethical, sustainable, and meaningful integration of AI in OT education and practice within the local setting.

Human ; Artificial Intelligence ; Occupational Therapy ; Philippines

BACKGROUND

Artificial Intelligence (AI) is increasingly transforming various fields, including healthcare. In occupational therapy (OT), Generative AI (GenAI) holds promise for enhancing clinical practice and patient outcomes. However, its successful integration depends not only on technological advancements but also on the perceptions, acceptance, and experiences of clinicians. Despite global interest, limited research exists on the perspectives of OT practitioners in the Philippines.

OBJECTIVE

A qualitative study will be conducted with a theoretical sample of 15 OT clinicians actively working across Metro Manila who are familiar with AI, excluding those in academic roles. The Technology Acceptance Model (TAM) will be used as a guiding framework to understand OT clinicians' attitudes towards the usage of GenAI. After a successful pilot test, one-on-one semi-structured interviews will be
conducted online. Data will be thematically analyzed using NVivo 15, following a coding framework

METHODS

A qualitative study will be conducted with a theoretical sample of 15 OT clinicians actively working across Metro Manila who are familiar with AI, excluding those in academic roles. The Technology Acceptance Model (TAM) will be used as a guiding framework to understand OT clinicians' attitudes towards the usage of GenAI. After a successful pilot test, one-on-one semi-structured interviews will be
conducted online. Data will be thematically analyzed using NVivo 15, following a coding framework

EXPECTED RESULTS

The study is expected to provide insights into the familiarity, experiences, attitudes, and intentions of OT clinicians in Metro Manila regarding AI use in clinical practice. Findings may identify perceived benefits, concerns, ethical and practical considerations, and factors influencing the adoption of AI, highlighting opportunities and barriers for its responsible integration into OT practice.

Human ; Artificial Intelligence ; Occupational Therapy

OBJECTIVES

This study aims to create a study protocol that will explore UST administrators’ perceptions of the benefits and risks of AI use in higher education learning environments.

METHODS

A qualitative descriptive design will be employed, using semi-structured interviews with at least fifteen administrators selected through purposive sampling. Audio-recorded interviews will be transcribed verbatim and subjected to thematic analysis using NVivo software

RESULTS

Administrators from different college-level fields perceive and engage with AI across various academic contexts. Exploring these perceptions will allow guidance in the development of coherent, contextually grounded institutional policies that promote responsible GenAI use and support digital leadership in Philippine higher education.

Human ; Artificial Intelligence ; Universities ; Software ; Administrative Personnel ; Intelligence ; Risk ; Policy

BACKGROUND

Generative artificial intelligence (GenAI) is increasingly integrated into healthcare, including occupational therapy (OT), with potential applications in its service delivery and clinical decision-making. While GenAI offers promising educational and clinical support, its generative nature introduces risks related to contextual accuracy, transparency, and ethical use, particularly within supervised clinical training settings where professional judgment is still developing. Empirical research examining GenAI integration in OT clinical training remains limited, especially within the Philippine context.

OBJECTIVE

This is a protocol for a study which aims to explore the perceptions of OT interns in Manila, Philippines, regarding GenAI integration in clinical training, including perceived benefits, challenges, and ethical considerations, guided by Rogers’ Diffusion of Innovations Theory

METHODS

This qualitative study protocol describes an exploratory design that will be used to gather rich and contextualized insights from 24 to 32 OT interns enrolled in universities in Manila with established institutional AI-use policies. Data will be collected through semi-structured online focus group discussions (FGD). Thematic analysis will be used with assistance from atlas.ti. 

EXPECTED RESULTS

The study is expected to generate meaningful themes that describe the perceptions of OT interns regarding the integration of GenAI within supervised clinical training contexts. Results are expected to reflect how interns perceive the role of GenAI in supporting clinical decision-making, as well as its perceived challenges and ethical concerns related to institutional policies, data privacy, reliability, and variability in AI-generated outputs.

Human ; Therapeutics ; Universities ; Volition ; Philippines ; Clinical Decision-making ; Artificial Intelligence

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Extracorporeal membrane oxygenation (ECMO) is primarily used in clinical practice to provide continuous extracorporeal respiratory and circulatory support for patients with severe heart and lung failure, thereby sustaining life. It is a key technology for managing severe heart failure and respiratory failure that are difficult to control. With the accumulation of clinical experience in ECMO for circulatory and/or respiratory support, as well as advancements in biomedical engineering technology, more portable and stable ECMO devices have been introduced into clinical use, benefiting an increasing number of critically ill patients. Although ECMO technology has become relatively mature, the timing of ECMO initiation, management of sudden complications, and monitoring and early warning of physiological indicators are critical factors that greatly affect the therapeutic outcomes of ECMO. This article reviews traditional methods and artificial intelligence techniques used in risk assessment related to ECMO, including the latest achievements and research hotspots. Additionally, it discusses future trends in ECMO risk management, focusing on six key areas: multi-center and prospective studies, external validation and standardization of model performance, long-term prognosis considerations, integration of innovative technologies, enhancing model interpretability, and economic cost-effectiveness analysis. This provides a reference for future researchers to build models and explore new research directions.
Extracorporeal Membrane Oxygenation ; Humans ; Artificial Intelligence ; Risk Assessment ; Respiratory Insufficiency/therapy* ; Heart Failure/therapy*