Transforming Pediatric Care Through AI: Bridging the Digital Divide in Health Informatics

Background:Minor head trauma is frequently presented to the pediatric emergency department. Despite the burden this injury poses on public health, evidence-based clinical guidelines on the assessment and management of pediatric minor head trauma remain unestablished, particularly in children below 2 years. We aimed to assess the diagnostic accuracy of a clinical decision rule (Pediatric Emergency Care Applied Research Network rule) and physician discretion in the recognition of practically important traumatic brain injury in children below 2 years of age presenting with minor head trauma to the emergency department.Methods:The medical records of children younger than 2 years presenting with head trauma to the emergency department were reviewed with Glasgow Coma Scale scores of 14–15. Practically important traumatic brain injury is a clinically essential traumatic brain injury including all cranial abnormalities (e.g. skull fracture) detected by computed tomography. All predictor variables of the Pediatric Emergency Care Applied Research Network rule and practically important traumatic brain injury outcomes were validated.Results:We enrolled and analyzed 433 children below 2 years. The most frequently observed mechanisms of injury in decreasing order were as follows: falls > 90 cm, head struck by high-impact objects, slip down, and automobile traffic accident. Of 224 children, positive findings were observed in 35 and 144 had one or more predictors of Pediatric Emergency Care Applied Research Network rule. The sensitivity, specificity, and negative likelihood ratio of the Pediatric Emergency Care Applied Research Network rule for practically important traumatic brain injury were 94.3%, 41.3%, and 0.14, respectively.Conclusion:The Pediatric Emergency Care Applied Research Network rule would assist in clinical decision-making to appropriately detect potential head injuries in children below 2 years, thereby reducing unnecessary performance of computed tomography scan.

To analyze the performance of commonly used blood tests in febrile infants ≤90 days of age to identify patients at low risk for invasive bacterial infection (bacterial pathogen in blood or cerebrospinal fluid) by duration of fever. We conducted a secondary analysis of a prospective single-center registry that includes all consecutive infants ≤90 days of age with fever without a source evaluated at 1 pediatric emergency department between 2008 and 2021. We defined 3 groups based on caregiver-reported hours of fever (<2, 2-12, and ≥12) and analyzed the performance of the biomarkers and Pediatric Emergency Care Applied Research Network, American Academy of Pediatrics, and Step-by-Step clinical decision rules. We included 2411 infants; 76 (3.0%) were diagnosed with an invasive bacterial infection. The median duration of fever was 4 (interquartile range, 2-12) hours, with 633 (26.3%) patients with fever of <2 hours. The area under the curve was significantly lower in patients with <2 hours for absolute neutrophil count (0.562 vs 0.609 and 0.728) and C-reactive protein (0.568 vs 0.760 and 0.812), but not for procalcitonin (0.749 vs 0.780 and 0.773). Among well-appearing infants older than 21 days and negative urine dipstick with <2 hours of fever, procalcitonin ≥0.14 ng/mL showed a better sensitivity (100% with specificity 53.8%) than that of the combination of biomarkers of Step-by-Step (50.0% and 82.2%), and of the American Academy of Pediatrics and Pediatric Emergency Care Applied Research Network rules (83.3% and 58.3%), respectively. The performance of blood biomarkers, except for procalcitonin, in febrile young infants is lower in fever of very short duration, decreasing the accuracy of the clinical decision rules.

Introduction:Minor head traumas constitute a significant part of childhood injuries. The incidence of intracranial pathologies in children with minor head trauma varies in the range of 3%–5%, but it is higher among younger infants. The criteria of the Pediatric Emergency Care Applied Research Network, Canadian Assessment of Tomography for Childhood Head Injury, and Children's Head Injury Algorithm for the Prediction of Important Clinical Events are the most frequently accepted clinical decision‐making criteria that were developed for selective computerized tomography requests. This study was conducted to assess the diagnostic performances of the Pediatric Emergency Care Applied Research Network, Canadian Assessment of Tomography for Childhood Head Injury, and Children's Head Injury Algorithm for the Prediction of Important Clinical Events criteria in Turkish society, determine their validity, and find the most suitable algorithm for cranial imaging in children with minor head trauma.Methods:This study retrospectively examined the data of patients under the age of 18 years who were admitted to the Emergency Medicine Department of Uludağ University Medical Faculty due to minor head trauma; 530 patients were included as they complied with the criteria. The exclusion criteria were being any trauma patients above the age of 18 years, Glasgow Coma Scale &lt;13, pregnant patients, hemorrhagic diathesis, using anticoagulants, patients with penetrant trauma, patients with priorly known brain tumor, and patients with neurological diseases. The patients were divided into group based on the Pediatric Emergency Care Applied Research Network, Canadian Assessment of Tomography for Childhood Head Injury, and Children's Head Injury Algorithm for the Prediction of Important Clinical Events Criteria.Results:Among all patients, 37.40% were female and 62.60% were male. Abnormal computed tomography findings such as epidural bleeding, subdural bleeding, and skull fractures were detected in 44 of the patients. The sensitivity of the Pediatric Emergency Care Applied Research Network criteria was 72.4%, the specificity was 54.5%, the sensitivity of the Canadian Assessment of Tomography for Childhood Head Injury criteria was 57.8%, the specificity was 50%, the sensitivity of the Children's Head Injury Algorithm for the Prediction of Important Clinical Events criteria was 87.7%, and the specificity was 20%.Conclusion:Given the populations to which the rules apply, it is understood that the Children's Head Injury Algorithm for the Prediction of Important Clinical Events criteria is more determinative in detecting pathological computed tomography outcomes compared to Pediatric Emergency Care Applied Research Network and Canadian Assessment of Tomography for Childhood Head Injury.

To develop the infrastructure and demonstrate the feasibility of conducting microarray-based RNA transcriptional profile analyses for the diagnosis of serious bacterial infections in febrile infants 60 days and younger in a multicenter pediatric emergency research network. We designed a prospective multicenter cohort study with the aim of enrolling more than 4000 febrile infants 60 days and younger. To ensure success of conducting complex genomic studies in emergency department (ED) settings, we established an infrastructure within the Pediatric Emergency Care Applied Research Network, including 21 sites, to evaluate RNA transcriptional profiles in young febrile infants. We developed a comprehensive manual of operations and trained site investigators to obtain and process blood samples for RNA extraction and genomic analyses. We created standard operating procedures for blood sample collection, processing, storage, shipping, and analyses. We planned to prospectively identify, enroll, and collect 1 mL blood samples for genomic analyses from eligible patients to identify logistical issues with study procedures. Finally, we planned to batch blood samples and determined RNA quantity and quality at the central microarray laboratory and organized data analysis with the Pediatric Emergency Care Applied Research Network data coordinating center. Below we report on establishment of the infrastructure and the feasibility success in the first year based on the enrollment of a limited number of patients. We successfully established the infrastructure at 21 EDs. Over the first 5 months we enrolled 79% (74 of 94) of eligible febrile infants. We were able to obtain and ship 1 mL of blood from 74% (55 of 74) of enrolled participants, with at least 1 sample per participating ED. The 55 samples were shipped and evaluated at the microarray laboratory, and 95% (52 of 55) of blood samples were of adequate quality and contained sufficient RNA for expression analysis. It is possible to create a robust infrastructure to conduct genomic studies in young febrile infants in the context of a multicenter pediatric ED research setting. The sufficient quantity and high quality of RNA obtained suggests that whole blood transcriptional profile analysis for the diagnostic evaluation of young febrile infants can be successfully performed in this setting.

The authors sought to describe the epidemiology of and risk factors for recurrent and high-frequency use of the emergency department (ED) by children. This was a retrospective cohort study using a database of children aged 0 to 17 years, inclusive, presenting to 22 EDs of the Pediatric Emergency Care Applied Research Network (PECARN) during 2007, with 12-month follow-up after each index visit. ED diagnoses for each visit were categorized as trauma, acute medical, or chronic medical conditions. Recurrent visits were defined as any repeat visit; high-frequency use was defined as four or more recurrent visits. Generalized estimating equations (GEEs) were used to measure the strength of associations between patient and visit characteristics and recurrent ED use. A total of 695,188 unique children had at least one ED visit each in 2007, with 455,588 recurrent ED visits in the 12 months following the index visits. Sixty-four percent of patients had no recurrent visits, 20% had one, 8% had two, 4% had three, and 4% had four or more recurrent visits. Acute medical diagnoses accounted for most visits regardless of the number of recurrent visits. As the number of recurrent visits per patient rose, chronic diseases were increasingly represented, with asthma being the most common ED diagnosis. Trauma-related diagnoses were more common among patients without recurrent visits than among those with high-frequency recurrent visits (28% vs. 9%; p<0.001). High-frequency recurrent visits were more often within the highest severity score classifications. In multivariable analysis, recurrent visits were associated with younger age, black or Hispanic race or ethnicity, and public health insurance. Risk factors for recurrent ED use by children include age, race and ethnicity, and insurance status. Although asthma plays an important role in recurrent ED use, acute illnesses account for the majority of recurrent ED visits.

Masters or Slaves? AI and the Future of Humanity

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The aim of this study was to understand the prevalence of alcohol and other substance use among teenagers in generalized samples. This study compared the alcohol and other substance use of adolescents enrolled in a screening study across 16 Pediatric Emergency Care Applied Research Network emergency departments (EDs) (ASSESS) with those sampled in 2 nationally representative surveys, the Youth Risk Behavior Surveillance System (YRBSS) and the National Survey of Drug Use and Health (NSDUH). The analysis includes 3362 ASSESS participants and 11,142 YRBSS and 12,086 NSDUH respondents. The ASSESS patients had a similar profile to the NSDUH sample, with small differences in marijuana and cocaine use and age at first tobacco smoking and smoking within the last 30 days and higher use of snuff or chewing tobacco. The YRBSS participants had higher rates of using marijuana, snuff/chewing tobacco, methamphetamine, and hallucinogens and higher smoking rates compared with ASSESS and NSDUH. Adolescents visiting Pediatric Emergency Care Applied Research Network EDs have substantial rates of substance use, similar to other nationally representative studies on this topic, although not as high as a school-based survey. Future ED studies should continue to investigate adolescent substance use, including exploring optimal methods of survey administration.

The Pediatric Emergency Care Applied Research Network (PECARN) prediction rule identifies febrile infants at low risk for serious bacterial infection (SBI). However, its impact on avoidable interventions in the emergency department remains unknown. To study the impact on lumbar puncture (LP) performance, empiric antibiotic use, and admissions after implementing a febrile infant clinical practice guideline for infants aged 29 to 60 days based on the PECARN prediction rule in the pediatric emergency department. This single center preintervention to postintervention study included infants 29 to 60 days old who presented with a chief complaint of fever from November 2018 to November 2021 and were assessed for SBI via blood culture and either urinalysis or urine culture. A new clinical practice guideline based on the PECARN prediction rule was implemented on December 2019. Lumbar puncture attempts, antibiotic administration, and admissions were compared preimplementation and postimplementation and in subgroups of low- and high-risk patients. Of 1597 (PRE: 785, POST: 812) infants presenting with fever, 1032 (PRE: 500, POST: 532) met inclusion criteria. Adoption of guideline recommendations (measured as procalcitonin order rate) was 89.7% in eligible infants postimplementation. Overall, there was a significant decrease in LPs (PRE: 30.6%, POST: 22.6%, P < 0.05) and no significant change in antibiotics or admissions. Among low-risk infants, there was a significant reduction in LPs (PRE: 17.2%, POST: 4.4%, P < 0.05) and antibiotics (PRE: 14.5%, POST: 4.1%; P < 0.05). There was no change in missed SBI (PRE: 3, POST: 2, P = 0.65). No cases of missed meningitis preimplementation or postimplementation were observed. After implementation of a guideline based on the PECARN prediction rule, we observed a reduction of LPs and antibiotics in low-risk infants. Overall, a decrease in LPs was observed, whereas antibiotic use and admissions remained unchanged.

To determine the prevalence of bacteremia and/or bacterial meningitis in febrile infants ≤60 days of age with positive urinalysis (UA) results. Secondary analysis of a prospective observational study of noncritical febrile infants ≤60 days between 2011 and 2019 conducted in the Pediatric Emergency Care Applied Research Network emergency departments. Participants had temperatures ≥38°C and were evaluated with blood cultures and had UAs available for analysis. We report the prevalence of bacteremia and bacterial meningitis in those with and without positive UA results. Among 7180 infants, 1090 (15.2%) had positive UA results. The risk of bacteremia was higher in those with positive versus negative UA results (63/1090 [5.8%] vs 69/6090 [1.1%], difference 4.7% [3.3% to 6.1%]). There was no difference in the prevalence of bacterial meningitis in infants ≤28 days of age with positive versus negative UA results (∼1% in both groups). However, among 697 infants aged 29 to 60 days with positive UA results, there were no cases of bacterial meningitis in comparison to 9 of 4153 with negative UA results (0.2%, difference -0.2% [-0.4% to -0.1%]). In addition, there were no cases of bacteremia and/or bacterial meningitis in the 148 infants ≤60 days of age with positive UA results who had the Pediatric Emergency Care Applied Research Network low-risk blood thresholds of absolute neutrophil count <4 × 103 cells/mm3 and procalcitonin <0.5 ng/mL. Among noncritical febrile infants ≤60 days of age with positive UA results, there were no cases of bacterial meningitis in those aged 29 to 60 days and no cases of bacteremia and/or bacterial meningitis in any low-risk infants based on low-risk blood thresholds in both months of life. These findings can guide lumbar puncture use and other clinical decision making.

Practice Variation in the Evaluation and Disposition of Febrile Infants ≤60Days of Age.

Background The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals’ competences with digital health (dHealth) and artificial intelligence (AI) technologies. Objective We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice. Methods A cross-sectional survey was administered online to students at the University of Montreal’s medical school, which has approximately 1,400 enrolled students. The survey included questions on students’ dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model. Results Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students’ perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05). Conclusions The study underscores the necessity of enhancing students’ competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.

Technology based on artificial intelligence (AI) is a revolutionary force that is changing economies, civilizations, and industries all over the world. AI, which has its roots in computer science and cognitive psychology, is a wide range of tools and methods designed to make robots capable of doing activities that have historically required human intellect. This abstract examines the many facets of artificial intelligence (AI) technology, including its fundamentals, uses, difficulties, and ramifications. Artificial Intelligence (AI) technology comprises several subfields such as robotics, computer vision, natural language processing, machine learning, and expert systems. Particularly, machine learning techniques have propelled incredible progress by allowing computers to learn from data and make judgments or predictions without the need for explicit programming. Natural language processing allows machines to comprehend, interpret, and produce human language, hence facilitating human-computer interaction. Machines can now see, analyze, and interpret visual data from the real world thanks to computer vision technology. Applications of AI technology may be found in a wide range of industries, including manufacturing, healthcare, finance, transportation, agriculture, education, and entertainment. AI-powered solutions help in drug discovery, medical imaging analysis, diagnosis, and customized therapy in the healthcare industry. AI algorithms are used in finance to power automated trading, fraud detection, risk assessment, and customer support. AI makes it possible for transportation to include predictive maintenance, traffic management, and driverless cars. Artificial Intelligence enhances supply chain management, quality assurance, and production processes in manufacturing. AI technology has the potential to revolutionize many industries, but it also comes with dangers and problems. These include privacy concerns, security hazards, ethical dilemmas, issues with prejudice and fairness, and effects on society and employment. Responsible AI methods, legal frameworks, multidisciplinary cooperation, and ethical standards are all necessary to meet these issues. Future prospects for AI technology development include the ability to solve challenging issues, spur creativity, increase productivity, and improve quality of life. But to fully utilize AI, one must take a comprehensive strategy that strikes a balance between the advancement of technology and ethical issues, human values, and social well-being. In summary, artificial intelligence (AI) technology is at the vanguard of innovation, presenting never-before-seen possibilities to transform whole sectors, spur economic expansion, and tackle global issues. AI has the ability to usher in a future of greater human-machine collaboration, innovation, and wealth through the promotion of collaboration, transparency, and ethical stewardship. the Ranking of the Artificial Intelligence using the TOPSIS Method . Interpretable Models is got the first rank whereas is the Ethical AI is having the Lowest rank. Keywords: Explainable AI (XAI), Interpretable Models, Ethical AI ,Responsible AI, Robustness and Adversarial Defense, Continual Learning, Federated Learning, Human-Centric AI, AI Governance and Policy

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored. The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare? A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice. Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships. There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships. In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.