人工智能

Alcohol is a group 1 carcinogen linked to seven cancers, yet awareness of this risk remains low in the United State. Identifying effective alcohol-cancer communication strategies is a public health priority. The objective of this study was to test the effects of message specificity (general vs. cancer-specific) and visual intensity (text-only, neutral pictorial, graphic pictorial) on message receptivity (attention, emotional reactions) and precursors of behavior (harm perception; intentions to reduce, limit, or stop drinking) among moderate and heavy alcohol consumers. Eight evidence-based text messages were created across two topics: general and cancer-specific harm. Artificial intelligence was used to generate pictorial versions at two intensities: neutral (symbolic, no visible disease) and graphic (explicit health consequences). In a 2025 online within-subject/between-subject crossover experiment, 639 US adult consumers (aged 21 years and older; 49.3% female) each viewed two randomly selected messages (one general, one cancer-specific) presented in three formats (text-only, neutral, and graphic; for six total formats), with counterbalanced order. Linear mixed-effects models were used to estimate differences by intensity, specificity, and drinking levels, reporting regression coefficients (&#x3b2;, 95% confidence intervals). Cancer-specific messages produced greater attention, emotional reactions, and intentions to reduce drinking than general messages (&#x3b2;&#xa0;=&#xa0;0.10-0.19; p&#xa0;<&#xa0;.05). Graphic pictorials outperformed neutral images on emotional and behavioral outcomes (&#x3b2;&#xa0;=&#xa0;0.19-0.22; p&#xa0;<&#xa0;.05). Moderate consumers showed stronger perceived harm and message responsiveness than heavy consumers (&#x3b2;&#xa0;=&#xa0;0.29-0.77; p&#xa0;<&#xa0;.05). Artificial intelligence-generated alcohol-cancer messages are feasible and effective in strengthening precursors to behavior change. Cancer-specific content and higher visual intensity enhance impact, particularly among moderate consumers, highlighting the importance of tailoring alcohol-cancer communication strategies to different audience characteristics. Many people in the United States are unaware that drinking alcohol increases the risk of several cancers. This study tested different ways of communicating the alcohol&#x2013;cancer link, including text&#x2010;only messages, messages with artificial intelligence&#x2013;generated images (neutral or graphic), and general versus cancer&#x2010;specific content; in addition, the authors examined whether responses differed between moderate drinkers and heavy drinkers. The authors observed that messages with images were more effective than those with text alone, especially when the images clearly showed health harm. Messages that named specific cancers (such as breast or colon cancer) were also more effective than general statements about cancer; however, heavy drinkers, who face the highest cancer risk, were less responsive overall. The findings suggest that effective alcohol&#x2013;cancer communication should carefully consider what the message says, how it is presented, and who the audience is. Nonstigmatizing, harm&#x2010;reduction messages that encourage small, realistic changes and are delivered in supportive settings, such as conversations with health care providers, may work better for heavy drinkers. The study also highlights the promise of artificial intelligence as a tool for developing and tailoring health messages. Future research should explore how different messages influence behavior over time.

We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on electroencephalography (EEG), termed EEGChat. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to be able to generate language. Our work thus demonstrates the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.

To evaluate comparative outcomes of artificial Intelligence (AI)-based and traditional-based teaching in medical education. The literature search was carried out in CENTRAL, CINAHL, Web of Science, MEDLINE, and EMBASE to identify randomized controlled trials (RCTs) comparing AI-based versus traditional-based teachings in medical education. Estimate of effect size was determined for knowledge score, skills score, and teaching satisfaction score via fixed-effect modelling. Fourteen RCTs enrolling 1116 students who received AI-based teaching (n&#xa0;=&#x2009;558) or traditional-based teaching (n&#xa0;=&#x2009;558) were included. The use of AI-based teaching was associated with significantly higher knowledge score (standardized mean difference (SMD): 0.36, 95% CI, 0.24-0.49, P&#xa0;<&#x2009;.00001), skills score (SMD: 0.78, 95% CI, 0.57-0.99, P&#xa0;<&#x2009;.00001), and teaching satisfaction score (SMD: 0.97, 95% CI, 0.66-1.29, P&#xa0;<&#x2009;.00001) compared to the traditional-based teaching. Subgroup analyses with respect to the practical course, theoretical course, duration of course shorter or longer than 1&#xa0;week were consistent with the main analyses. Meta-regression analysis demonstrated that practical course significantly increased estimate effect for knowledge score (P&#x2009;=&#x2009;.002) and skills score (P&#x2009;=&#x2009;.0001). Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. However, the available evidence may be subject to publication and reporting bias with high between-study heterogeneity. Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings. Key messages What is already known on this topic: Growing evidence from randomized controlled trials demonstrated positive impact of artificial intelligence (AI) in medical education when compared to the traditional approaches. What this study adds: Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. How this study might affect research, practice, or policy: This study suggests that Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings.

Hepatocellular carcinoma (HCC) is a malignant tumor that is common worldwide. It is characterized by high incidence and mortality rates. Interventional therapy is a minimally invasive treatment for HCC that offers diverse methods that cover different stages. Because of the significant heterogeneity of tumors, even at the same stage, the effectiveness of interventional therapy can vary greatly, which makes it difficult for clinicians to determine the optimal treatment plan before treatment. Increasing evidence suggests that tumor-related imaging characteristics are correlated with biological functions and can be used to predict different subtypes of HCC and reflect their heterogeneity. In recent years, artificial intelligence (AI) has received widespread attention and been applied widely. AI can automatically extract features from medical images, objectively quantifying low-dimensional to high-dimensional information about tumors, which helps to directly or indirectly predict prognostic stratification and treatment response to interventional therapy. Furthermore, when AI integrates high-dimensional quantifiable information from imaging data with multimodal clinical and molecular data, its accuracy and interpretability improve significantly. Although image-derived AI models have achieved good performance and have broad prospects for application in the prognosis and treatment of HCC, their clinical implementation has limitations, including data and imaging standardization, model interpretability, and the need for multicenter validation. This review summarizes the latest advancements in medical image-driven AI in the prognostic stratification and efficacy prediction of interventional therapy for HCC, and outlines the main challenges that need to be addressed and good prospects for application.

The pervasive adoption of Generative Artificial Intelligence tools in professional settings raises a question that those responsible for training can no longer avoid: what happens to expert intuition - that form of embodied, tacit knowledge built through years of practice and error - when the most cognitively demanding tasks are systematically delegated to a machine? This article argues that the risk is not technical but formative: it concerns the mechanism through which deep professional competence is constructed, with concrete implications for the design of professional development pathways in healthcare organisations.

Artificial intelligence is now embedded across the scientific research and publishing ecosystem, influencing discovery, analysis, knowledge translation, authorship, peer review, and editorial workflows. In cardiovascular and biomedical sciences, these developments offer substantial opportunities to accelerate knowledge generation, integrate complex datasets, and improve efficiency and consistency. At the same time, they introduce new risks related to bias, transparency, data integrity, and authorship responsibility, potentially endangering trust in the scientific record. This commentary examines the evolving role of AI in biomedical publishing, with particular attention to generative models and machine learning tools. We review both benefits and limitations, highlight risks such as fabricated content, biased outputs, and erosion of accountability, and discuss why traditional detection approaches are insufficient. Instead, we argue for a shift toward transparency, provenance, and enforceable human responsibility as the core principles guiding AI use, ensuring that AI strengthens rather than undermines scientific rigour and public trust. We outline practical expectations for authors, reviewers, editors, and publishers, with emphasis on reporting standards, reproducibility under rapidly evolving model versions, and the conflict-of-interest implications of AI tooling for the editorial process itself.

Drug safety assessment, particularly in the post-marketing setting, is especially vulnerable to analytic misjudgment because it relies on heterogeneous evidence streams, incomplete data, infrequent events, and decisions made under substantial uncertainty. Recurring sources of error include misinterpretation of conditional probabilities, conflation of association with causation, inappropriate denominator and comparator selection, inadequate consideration of background incidence and confounding, aggregation artifacts such as Simpson's paradox, and overinterpretation of exploratory findings arising from multiplicity or repeated testing. Misjudgment may be further amplified by spontaneous reporting data that lack explicit exposure denominators and are susceptible to reporting bias, by fragile or incomplete meta-analyses, and by premature regulatory or public responses to weak or incompletely contextualized signals. Using selected real-world case studies and conceptual examples, this narrative review illustrates how such errors arise and propagate across clinical, regulatory, and public domains, and how they can materially influence causality assessment and decision making. The paper also discusses how artificial intelligence (AI), if implemented without transparency, bias assessment, and clinical oversight, may amplify rather than reduce these vulnerabilities. Greater analytic discipline, clearer communication of uncertainty, triangulation across evidence streams, and careful governance of emerging AI-enabled tools are needed to support more reliable drug safety evaluation.

The integration of artificial intelligence (AI) into clinical research challenges traditional informed consent (IC) frameworks due to algorithmic complexity, opacity, and adaptive nature. While public demand for transparency regarding AI use in healthcare is high, current ethical guidelines lack specificity, and no assessment exists of AI representation in IC documentation within the trial registry. This study aimed to evaluate the prevalence, clarity, and completeness of AI-related consent disclosures in clinical trials registered on ClinicalTrials.gov and to propose a framework for enhanced patient digital literacy and ethical robustness. We conducted a cross-sectional content analysis of 114 AI-involved clinical trials with publicly available IC documents from ClinicalTrials.gov (searched on June 21, 2025). We assessed AI-specific disclosures, readability (SMOG index), document length, visual aid use, and data governance protocols against WHO/NIH standards. We also refined an AI risk framework encompassing model autonomy, departure from standards of care, patient-facing interaction, and clinical risk, scoring each trial on a 3-tier scale. Over half (55%) of ICs failed to disclose the AI type or usage, and 16.4% omitted risks entirely. A significant discrepancy existed between trial registry and IC reporting of AI methods. Only 14% of ICs met dual criteria for brevity (<15,000 characters) and readability (SMOG <13). Higher-risk trials did not demonstrate improved readability (Spearman's p>0.05). Only 11.4% of ICs included visual aids, and their inclusion was not correlate with lower reading difficulty. Data handling protocols post-withdrawal were inconsistent: 51 ICs provided no information, 30 specified data destruction, 29 allowed continued use, and only 4 (3.5%) offered participants a choice. Cited data protection laws varied widely, with no dominant standard. Current IC practices in AI clinical trials registered on ClinicalTrials.gov show a notable disconnect from ethical principles, with deficits in transparency, readability, and participant control over data. Our findings indicate a need for more standardized, participant&#x2011;centered consent practices. We propose the Minimum Requirements for Informed Consent in AI&#x2011;Related Clinical Trials (MRIC&#x2011;AI) as one possible framework to improve consent quality. However, cautions should be noted that these findings are limited to publicly available consent documents in the registry, and may differ from final onsite versions.

The prevalence of gestational diabetes mellitus (GDM) continues to rise, necessitating reliable and effective self-management strategies to improve maternal and neonatal outcomes. However, current self-management models face challenges, including insufficient data monitoring and analysis, delayed modifications to treatment protocols, and excessive reliance on manual processes. With the expanding application of artificial intelligence (AI) in healthcare, its potential value in the self-management of GDM has attracted increasing attention. This systematic review aimed to synthesize the evidence on the application of AI technologies in the self-management of patients with GDM. This systematic review was conducted in April 2025 and included comprehensive literature searches across PubMed, Embase, The Cochrane Library, Scopus, Web of Science, CINAHL, CBM, CNKI, VIP, and Wanfang databases. The search strategy combined Medical Subject Headings and free-text terms related to GDM, AI, machine learning, and self-management. Quantitative studies that explored the application of AI in the self-management of patients with GDM were included, including randomized controlled trials and cohort studies. Two researchers independently performed study selection and data extraction, followed by quality assessment using risk-of-bias instruments appropriate for each study design. Data were synthesized using a narrative approach combined with thematic synthesis. The initial search yielded 18,973 records. After stepwise screening, 10 studies were included. A total of 645 patients with GDM completed AI-assisted interventions (from 661 initially enrolled), along with 864 control participants (from 877 enrolled). A variety of AI technologies were employed, including expert systems, machine learning, and natural language processing. Their primary functions included abnormality detection and alert triggering, personalized treatment plan generation and adjustment, and data integration and management. The studies reported multiple outcomes. Regarding health outcomes, six studies reported that AI interventions were associated with improved glycemic control, although heterogeneity was observed in delivery outcomes and insulin utilization rates. In terms of adherence, AI interventions tended to increase the frequency of blood glucose monitoring and data upload rates. Regarding system usability, limited data suggested that the accuracy of dietary recommendations and detection of blood glucose abnormalities was satisfactory, whereas the adoption rate of insulin treatment adjustment recommendations was relatively low. User satisfaction was generally high. Facilitators for implementation included technological advantages, user experience, and external support, whereas barriers included data integration and quality issues, technical and hardware or software limitations, patient acceptance, and difficulties in clinical integration. Preliminary evidence suggests that AI may contribute to the self-management of GDM; however, its practical application faces several obstacles. Future efforts should focus on conducting high-quality clinical research and evaluating implementation-related experiences to facilitate the integration of AI into GDM self-management.

Tremendous improvement in the use of artificial intelligence has opened new opportunities to analyze the data obtained from electronic health records and imaging. New technologies have tried to overcome obstacles to implement guidelines and recommendations. This review aims to describe the recent progress in the use of machine learning and new technologies in the field of nutrition of the critically ill. Increase in data availability, ability to extract these data and analyze them using machine learning has allowed data scientists together with ICU specialists to improve nutritional screening and assessment and to predict occurrence of obstacles like enteral feeding intolerance or refeeding hypophosphatemia. In addition, new technologies can ensure nasogastric tube positioning and enteral feeding efficacy. Integrated platforms can integrate nutritional needs with most adequate prescriptions and modulate the nutritional administration according to the patient's tolerance and requirements. Analysis of continuous recording of imaging obtained from ultrasound can also predict gastric intolerance. Using machine learning, numerous algorithms and nomograms have been suggested to predict enteral feeding intolerance but validation of these predictions is still required. New technologies integrating energy requirements and delivery of the optimal enteral feeding are very promising.

The increasing complexity of healthcare systems management requires the development of advanced methodologies to support efficient resource allocation, service delivery, and strategic planning. Artificial intelligence has emerged as an important tool in this domain, offering capabilities to model demand, predict capacity requirements, and inform operational decisions through data-driven insights. This article provides a comprehensive scoping review of AI-based approaches for demand and capacity modelling in healthcare systems. Specifically, it examines AI methods applied to key demand prediction tasks, including outpatient activity, emergency department attendances, hospital admissions and readmissions, and length of stay, as well as capacity planning for beds, workforce, equipment, and other critical resources. The review reports trends in AI models design, learning paradigms, performance reporting, and data usage, and highlights the relationship between demand modelling and downstream capacity predictions. In addition, the paper analyses data infrastructure requirements, commonly used datasets, and the growing role of explainable AI in supporting transparency and trust. Despite recent advances in the field, the integration of AI into healthcare systems faces significant challenges, including concerns related to privacy, ethics, data quality, interpretability, bias, scalability, policy variation, and data interoperability. Addressing these challenges is essential to develop sustainable, fair and resilient healthcare systems. Our review highlights the current state and gaps in the literature, and proposes future directions for advancing the use of AI in healthcare management systems are reviewed.

Emergency physicians experience substantial documentation burden, contributing to physician burnout. Human scribes reduce documentation workload but are expensive and pose staffing challenges. Ambient artificial intelligence (AI) scribes offer a potential alternative by automating note generation from clinician-patient conversations using AI. We compared ambient AI and human scribes against encounters with no scribe on emergency physicians' documentation time and clinical productivity. This retrospective cross-sectional observational study evaluated emergency department encounters from January 2025 to September 2025 at 4 hospitals within a large integrated health care system. Encounters were categorized as ambient AI scribe, human scribe, or no scribe. Patient demographics, documentation time, work relative value units (wRVUs), and shift data were extracted. Attending documentation time was modeled using median quantile regression with standard errors clustered at the physician level and controlling for encounter-level variables. Clinical productivity measured as total wRVUs per shift hour was modeled using generalized estimating equations adjusting for physician and shift-level variables. Among 198,178 emergency department encounters, 8,489 (4.3%) used ambient AI scribes, 15,947 (8.0%) used human scribes, and 173,742 (87.7%) had no scribe. Median patient age was 49 years (interquartile range 30 to 68 years), and 53.1% were female. Compared with encounters with no scribe, ambient AI scribes were associated with a 1.6-minute reduction in adjusted median attending documentation time per note (95% confidence interval 0.3 to 2.9), whereas human scribes were associated with a 3.3-minute reduction (95% confidence interval 2.3 to 4.3). Total wRVUs per shift hour did not differ among groups. Ambient AI and human scribes were associated with reduced physician documentation time. Clinical productivity did not differ between study groups.

Embodied intelligence-artificial intelligence instantiated in physical or virtual bodies that can perceive, communicate, and interact with users and their environments-has been increasingly applied in health care. However, the evidence base remains fragmented because of inconsistent terminology, diverse embodiment forms, and limited synthesis of application domains, target populations, care settings, acceptability, and effectiveness. This fragmentation constrains conceptual clarity and translation into routine health care practice. This scoping review aimed to systematically map the applications of embodied intelligence in health care by classifying embodiment forms, identifying major functional domains, describing target populations and implementation settings, and synthesizing the available evidence on acceptability and effectiveness. This scoping review followed the Arksey and O'Malley framework, with enhancements by Levac et al, and was reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) guidelines. Seven electronic databases were searched from database inception to December 2025, supplemented by gray literature searches and backward citation screening. Eligible studies were primary empirical studies published in English or Chinese that examined embodied intelligence in health care contexts. Two reviewers independently screened records and charted data using a pilot-tested standardized form. Descriptive statistics and thematic synthesis were applied. No formal critical appraisal was conducted because the aim was to map the breadth and characteristics of the evidence base. A total of 83 studies were included. Five embodiment forms were identified: virtual humanoid agents (32/83, 38.6%), physical humanoid robots (32/83, 38.6%), virtual animal-shaped agents (1/83, 1.2%), physical animal robots (13/83, 15.7%), and mechanical robots (5/83, 6%). Applications clustered into 3 functional domains: health management and health education (40/83, 48.2%), mental health promotion (37/83, 44.6%), and physiological health promotion (6/83, 7.2%). Older adults were the most frequently targeted population (45/83, 54.3%). Interventions were mainly implemented in home settings, care homes, laboratories, and hospitals. Twenty-two randomized controlled trials reported generally beneficial effects on health behaviors, mental health outcomes, or cognitive function, although outcome measures were heterogeneous. Twelve studies examined acceptability and generally reported favorable user acceptance. This scoping review provides the first comprehensive synthesis of embodied intelligence in health care using a unified classification of forms, functional domains, populations, and application settings. The findings indicate that embodied intelligence is most mature in "health management and health education" and "mental health promotion," with increasing real-world deployment in home and care home settings. By consolidating fragmented evidence and standardizing terminology, this review offers a practical foundation for clinicians, nurses, and policymakers to support the implementation of embodied intelligence in routine health care. Evidence is limited by heterogeneous outcome measures, many lab-based evaluations, and the absence of formal quality appraisal, underscoring the need for standardized outcome measures, rigorous randomized controlled trials, and longitudinal evaluations to enable scalable and ethically grounded real-world adoption.

The increasing use of oral anticancer drugs (OADs) in cancer therapy shifts greater responsibility towards patients, thereby also placing a higher informational burden on them. While intensified pharmacological/pharmaceutical care programs have proven beneficial for patients undergoing OAD treatment, their universal availability is currently limited. Given that patients frequently seek health information online, AI-powered chatbots may present a promising resource to address these increasing, yet often unmet information needs. This study aims to evaluate the readability, completeness of relevant information, and accuracy provided by AI-powered chatbots in response to patient questions about OAD treatment. Microsoft Bing's Copilot and Google's Gemini were queried in June 2024 on four patient questions regarding ten commonly prescribed and ten recently approved OADs in triplicate. Readability of chatbot answers was assessed using the Flesch reading-ease score (scale 0-100). Completeness of relevant information and accuracy were evaluated based on corresponding standardized written patient information materials. Both chatbots' answers demonstrated low readability according to the overall mean Flesch reading-ease scores of 38.8 (Copilot) and 50.9 (Gemini). Overall median completeness of relevant information of Copilot's and Gemini's answers was 61.1% (IQR, 35.3-78.7%) and 73.8% (IQR, 50.0-100.0%), respectively. Conversely, accuracy of chatbot answers was consistently high, with an overall median accuracy of 100.0% (IQR, 83.3-100.0%) for Copilot and 100.0% (IQR, 98.5-100.0%) for Gemini. AI-powered chatbots provide overall accurate information on OADs. However, their moderate completeness of relevant information and low readability may limit their current practical utility in meeting cancer patients' information need.

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Biosimilar development is undergoing regulatory change, with growing emphasis on analytical comparability and targeted clinical pharmacology rather than routine comparative efficacy studies. A focused review is therefore needed to understand how global regulatory expectations are evolving, where they are converging, and what this means for future development. This review examines biosimilar regulatory frameworks across agencies, including the FDA, EMA, PMDA, WHO, Health Canada, and ANVISA. It outlines the shift toward a stepwise, totality-of-evidence approach in which analytical similarity, functional characterization, pharmacokinetic/pharmacodynamic comparability, and immunogenicity assessment provide the basis for establishing biosimilarity. It also considers recent regulatory changes supporting a reduced role for comparative efficacy studies, broader use of foreign comparators and reliance pathways, and the emerging application of artificial intelligence in comparability assessment and model-informed development. Relevant regulatory documents and peer-reviewed literature were reviewed to summarize current trends, differences, and likely future directions. For many well-characterized biosimilars, particularly monoclonal antibodies, robust analytical and clinical pharmacology data can address most uncertainty, making routine comparative efficacy studies less necessary in many cases. Greater alignment in comparator policies, study expectations, and responsible use of artificial intelligence could help streamline development, reduce duplication, and improve patient access.

Helminth infections are uncommon in the United States, yet accurate detection remains critical. Conventional ova-and-parasite examinations are labor-intensive and prone to error in low-prevalence settings. We evaluated the impact of implementing an artificial intelligence (AI)-based wet mount screening platform (WM-AI) in a high-volume reference laboratory. Detection rates, turnaround times, and specimen characteristics were compared across two 6-month periods before and after WM-AI implementation. Post-implementation, helminth detection increased from 29 to 104 patients, and client-level positivity rose from 4.5% to 12.3% (P < 0.0001). Enterobius vermicularis showed a fivefold increase in positivity (0.03% vs 0.15%, P < 0.0001). Median turnaround time improved from 5.2 to 4.0 days (P < 0.0001). Most infections were identified in the first or only specimen submitted, and over half of positive specimens contained &#x2264;5 eggs or larvae, highlighting the challenge of detecting low-intensity infections. AI-assisted screening enhanced operational efficiency and diagnostic yield, suggesting improved sensitivity for low-burden infections. These findings support the integration of AI tools in parasitology workflows.IMPORTANCEThe data presented in our manuscript are observational after adopting a more sensitive assay in our parasitology workflow, specifically the use of artificial intelligence (AI) to pre-screen concentrated stool specimens for parasites. The notable increase in the positivity of helminth cases in our lab, which is a large reference laboratory, indicates that adopting AI as a screening tool in a parasitology laboratory can increase sensitivity of pathogenic parasites, especially helminths.