A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records

Stressful life events in electronic health records: a scoping review

OBJECTIVES

Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.

MATERIALS AND METHODS

Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by 2 independent reviewers.

RESULTS

Five hundred fifty-seven unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n = 36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n = 15, 21.4%), extraction of events from free-text notes (n = 12, 17.1%), discussing privacy and other issues of storing life events (n = 5, 7.1%), and new EHR features related to life events (n = 4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n = 7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n = 7, 46.7%) were discussing interventions related to social determinants of health in general.

DISCUSSION AND CONCLUSIONS

Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.

Artificial Intelligence in Pediatrics: Learning to Walk Together

In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.

Integrating Artificial Intelligence in Pediatric Healthcare: Parental Perceptions and Ethical Implications

BACKGROUND

Our study aimed to explore the way artificial intelligence (AI) utilization is perceived in pediatric medicine, examining its acceptance among patients (in this case represented by their adult parents), and identify the challenges it presents in order to understand the factors influencing its adoption in clinical settings.

METHODS

A structured questionnaire was applied to caregivers (parents or grandparents) of children who presented in tertiary pediatric clinics.

RESULTS

The most significant differentiations were identified in relation to the level of education (e.g., aversion to AI involvement was 22.2% among those with postgraduate degrees, 43.9% among those with university degrees, and 54.5% among those who only completed high school). The greatest fear among respondents regarding the medical use of AI was related to the possibility of errors occurring (70.1%).

CONCLUSIONS

The general attitude toward the use of AI can be considered positive, provided that it remains human-supervised, and that the technology used is explained in detail by the physician. However, there were large differences among groups (mainly defined by education level) in the way AI is perceived and accepted.

Application of a Machine Learning Algorithm in Prediction of Abusive Head Trauma in Children

PURPOSE

We explored the application of a machine learning algorithm for the timely detection of potential abusive head trauma (AHT) using the first free-text note of an encounter and demographic information.

METHODS

First free-text physician notes and demographic information were collected for children under 5 years of age at a Level 1 Trauma Center. The control group, which included patients with head/neck injury, was compared to those with AHT diagnosed by the Child Protective Team. Differential scores accounted for words overrepresented in AHT patient vs. control notes. Sentiment scores were reflective of note positivity/negativity and subjectivity scores accounted for note subjectivity/objectivity. The composite scores reflected the patient's differential score modified by the subjectivity score. Composite, sentiment, and subjectivity scores combined with demographic information trained a Random Forest (RF) machine learning algorithm to predict AHT.

RESULTS

Final composite scores with demographic information were highly associated with AHT in a test dataset. The control group included 587 patients and the test group included 193 patients. Combining composite scores with demographic information into the RF model improved AHT classification area under the curve (AUC) from 0.68 to 0.78, with an overall accuracy of 84%. Feature importance analysis of our RF model revealed that composite score, sentiment, age, and subjectivity were the most impactful predictors of AHT. The sentiment was not significantly different between control and AHT notes (p = 0.87), while subjectivity trended higher for AHT notes (p = 0.081).

CONCLUSION

We conclude that a machine learning algorithm can recognize patterns within free-text notes and demographic information that aid in AHT detection in children.

LEVEL OF EVIDENCE

III.

Stressful life events in electronic health records: a scoping review

Objective

Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer two major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.

Materials and Methods

Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by two independent reviewers.

Results

557 unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n=36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n=15, 21.4%), extraction of events from free-text notes (n=12, 17.1%), discussing privacy and other issues of storing life events (n=5, 7.1%), and new EHR features related to life events (n=4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n=7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n=7, 46.7%) were discussing interventions related to social determinants of health in general.

Discussion and Conclusions

Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.

Stressful life events in electronic health records: a scoping review

Objective

Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer two major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.

Materials and Methods

Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by two independent reviewers.

Results

557 unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n=36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n=15, 21.4%), extraction of events from free-text notes (n=12, 17.1%), discussing privacy and other issues of storing life events (n=5, 7.1%), and new EHR features related to life events (n=4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n=7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n=7, 46.7%) were discussing interventions related to social determinants of health in general.

Discussion and Conclusions

Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.

Initial development of tools to identify child abuse and neglect in pediatric primary care

BACKGROUND

Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN. Strategies that summarize EHR data to identify CAN and convey this to providers has potential to mitigate CAN-related sequelae. This study used expert review/consensus and Natural Language Processing (NLP) to develop and test a lexicon to characterize children who have experienced or are at risk for CAN and compared machine learning methods to the lexicon + NLP approach to determine the algorithm's performance for identifying CAN.

METHODS

Study investigators identified 90 CAN terms and invited an interdisciplinary group of child abuse experts for review and validation. We then used NLP to develop pipelines to finalize the CAN lexicon. Data for pipeline development and refinement were drawn from a randomly selected sample of EHR from patients seen at pediatric primary care clinics within a U.S. academic health center. To explore a machine learning approach for CAN identification, we used Support Vector Machine algorithms.

RESULTS

The investigator-generated list of 90 CAN terms were reviewed and validated by 25 invited experts, resulting in a final pool of 133 terms. NLP utilized a randomly selected sample of 14,393 clinical notes from 153 patients to test the lexicon, and .03% of notes were identified as CAN positive. CAN identification varied by clinical note type, with few differences found by provider type (physicians versus nurses, social workers, etc.). An evaluation of the final NLP pipelines indicated 93.8% positive CAN rate for the training set and 71.4% for the test set, with decreased precision attributed primarily to false positives. For the machine learning approach, SVM pipeline performance was 92% for CAN + and 100% for non-CAN, indicating higher sensitivity than specificity.

CONCLUSIONS

The NLP algorithm's development and refinement suggest that innovative tools can identify youth at risk for CAN. The next key step is to refine the NLP algorithm to eventually funnel this information to care providers to guide clinical decision making.

Artificial Intelligence and Child Abuse and Neglect: A Systematic Review

All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: "what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?"; "which is the risk of bias of the included articles?". The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data.

The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review

Objective

This review examines the literature on improving clinician satisfaction with a focus on what has been most effective in improving experience from the perspective of clinicians, and the potential role that virtual triage (VT) technology can play in delivering positive clinician experiences that improve clinical care, and bring value to health care delivery organizations (HDOs).

Methods

Review and synthesis of evidence on clinician satisfaction indicating a potential for VT to favorably impact clinician experience, sense of effectiveness, efficiency, and reduction of administrative task burden. Analysis considers how to conceptualize and the value of improving clinician experience, leading clinician dissatisfiers, and the potential role of VT in improving clinician experience/satisfaction.

Results

Contributors to poor clinician experience/satisfaction where VT could have a beneficial impact include better managing resource limitations, administrative workload, lack of care coordination, information overload, and payer interactions. VT can improve clinician experience through the technology's ability to leverage real-time actionable data clinicians can use, streamlining patient-clinician communications, personalizing care delivery, optimizing care coordination, and better aligning digital/virtual services with clinical practice. From an organizational perspective, improvements in clinician experience and satisfaction derive from establishing an effective digital back door, increasing the clinical impact of and satisfaction derived from telemedicine and virtual care, and enhancing clinician centricity.

Conclusions

By embracing digital transformation and implementing solutions such as VT that focus on improving patient and clinician experience, HDOs can address barriers to delivery of high-quality, efficient, and cost-effective care. VT is a digital health tool that can create a more streamlined and satisfying experience for clinicians and the patients they care for. VT is a technology solution that can help clinicians make faster more informed decisions, reduces avoidable care, improves communication with patients and within care teams, and lowers their administrative burden so they have more quality time to care for patients.

May Artificial Intelligence Influence Future Pediatric Research?-The Case of ChatGPT

BACKGROUND

In recent months, there has been growing interest in the potential of artificial intelligence (AI) to revolutionize various aspects of medicine, including research, education, and clinical practice. ChatGPT represents a leading AI language model, with possible unpredictable effects on the quality of future medical research, including clinical decision-making, medical education, drug development, and better research outcomes.

AIM AND METHODS

In this interview with ChatGPT, we explore the potential impact of AI on future pediatric research. Our discussion covers a range of topics, including the potential positive effects of AI, such as improved clinical decision-making, enhanced medical education, faster drug development, and better research outcomes. We also examine potential negative effects, such as bias and fairness concerns, safety and security issues, overreliance on technology, and ethical considerations.

CONCLUSIONS

While AI continues to advance, it is crucial to remain vigilant about the possible risks and limitations of these technologies and to consider the implications of these technologies and their use in the medical field. The development of AI language models represents a significant advancement in the field of artificial intelligence and has the potential to revolutionize daily clinical practice in every branch of medicine, both surgical and clinical. Ethical and social implications must also be considered to ensure that these technologies are used in a responsible and beneficial manner.

Using natural language processing to identify child maltreatment in health systems

BACKGROUND

Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance.

OBJECTIVES

To examine whether using natural language processing (NLP) in outpatient chart notes can identify cases of CM not documented by ICD diagnosis code, the overlap between the coding of child maltreatment by ICD and NLP, and any differences by age, gender, or race/ethnicity.

METHODS

Outpatient chart notes of children age 0-18 years old within Kaiser Permanente Washington (KPWA) 2018-2020 were used to examine a selected set of maltreatment-related terms categorized into concept unique identifiers (CUI). Manual review of text snippets for each CUI was completed to flag for validated cases and retrain the NLP algorithm.

RESULTS

The NLP results indicated a crude rate of 1.55 % to 2.36 % (2018-2020) of notes with reference to CM. The rate of CM identified by ICD code was 3.32 per 1000 children, whereas the rate identified by NLP was 37.38 per 1000 children. The groups that increased the most in identification of maltreatment from ICD to NLP were adolescents (13-18 yrs. old), females, Native American children, and those on Medicaid. Of note, all subgroups had substantially higher rates of maltreatment when using NLP.

CONCLUSIONS

Use of NLP substantially increased the estimated number of children who have been impacted by CM. Accurately capturing this population will improve identification of vulnerable youth at high risk for mental health symptoms.

Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers

BACKGROUND

Child abuse and neglect, once viewed as a social problem, is now an epidemic. Moreover, health providers agree that existing stereotypes may link racial and social class issues to child abuse. The broad adoption of electronic health records (EHRs) in clinical settings offers a new avenue for addressing this epidemic. To reduce racial bias and improve the development, implementation, and outcomes of machine learning (ML)-based models that use EHR data, it is crucial to involve marginalized members of the community in the process.

OBJECTIVE

This study elicited Black and Latinx primary caregivers' viewpoints regarding child abuse and neglect while living in underserved communities to highlight considerations for designing an ML-based model for detecting child abuse and neglect in emergency departments (EDs) with implications for racial bias reduction and future interventions.

METHODS

We conducted a qualitative study using in-depth interviews with 20 Black and Latinx primary caregivers whose children were cared for at a single pediatric tertiary-care ED to gain insights about child abuse and neglect and their experiences with health providers.

RESULTS

Three central themes were developed in the coding process: (1) primary caregivers' perspectives on the definition of child abuse and neglect, (2) primary caregivers' experiences with health providers and medical documentation, and (3) primary caregivers' perceptions of child protective services.

CONCLUSIONS

Our findings highlight essential considerations from primary caregivers for developing an ML-based model for detecting child abuse and neglect in ED settings. This includes how to define child abuse and neglect from a primary caregiver lens. Miscommunication between patients and health providers can potentially lead to a misdiagnosis, and therefore, have a negative impact on medical documentation. Additionally, the outcome and application of the ML-based models for detecting abuse and neglect may cause additional harm than expected to the community. Further research is needed to validate these findings and integrate them into creating an ML-based model.

Machine learning approaches for electronic health records phenotyping: a methodical review

OBJECTIVE

Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used.

MATERIALS AND METHODS

We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies.

RESULTS

Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions.

DISCUSSION

Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released.

CONCLUSION

Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.

Screening for Social Determinants of Health: Active and Passive Information Retrieval Methods

Screening for social determinants of health (SDOH) is recommended, but numerous barriers exist to implementing SDOH screening in clinical spaces. In this study, the authors identified how both active and passive information retrieval methods may be used in clinical spaces to screen for SDOH and meet patient needs. The authors conducted a retrospective sequential cohort analysis comparing the active identification of SDOH through a patient-led digital manual screening process completed in primary care offices from September 2019 to January 2020 and passive identification of SDOH through natural language processing (NLP) from September 2016 to August 2018, among 1735 patients at a large midwestern tertiary referral hospital system and its associated outlying primary care and outpatient facilities. The percent of patients identified by both the passive and active identification methods as experiencing SDOH varied from 0.3% to 4.7%. The active identification method identified social integration, domestic safety, financial resources, food insecurity, transportation, housing, and stress in proportions ranging from 5% to 36%. The passive method contributed to the identification of financial resource issues and stress, identifying 9.6% and 3% of patients to be experiencing these issues, respectively. SDOH documentation varied by provider type. The combination of passive and active SDOH screening methods can provide a more comprehensive picture by leveraging historic patient interactions, while also eliciting current patient needs. Using passive, NLP-based methods to screen for SDOH will also help providers overcome barriers that have historically prevented screening.

Technology-Based Mental Health Interventions for Domestic Violence Victims Amid COVID-19

Introduction: Domestic violence is a threat to human dignity and public health. Mounting evidence shows that domestic violence erodes personal and public health, spawning issues such as lifelong mental health challenges. To further compound the situation, COVID-19 and societies’ poor response to the pandemic have not only worsened the domestic violence crisis but also disrupted mental health services for domestic violence victims. While technology-based health solutions can overcome physical constraints posed by the pandemic and offer timely support to address domestic violence victims’ mental health issues, there is a dearth of research in the literature. To bridge the research gap, in this study, we aim to examine technology-based mental health solutions for domestic violence victims amid COVID-19. Methods: A literature review was conducted to examine solutions that domestic violence victims can utilize to safeguard and improve their mental health amid COVID-19. Databases including PubMed, PsycINFO, and Scopus were utilized for the literature search. The search was focused on four themes: domestic violence, mental health, technology-based interventions, and COVID-19. A reverse search of pertinent references was conducted in Google Scholar. The social ecological model was utilized to systematically structure the review findings. Results: The findings show that a wide array of technology-based solutions has been proposed to address mental health challenges faced by domestic violence victims amid COVID-19. However, none of these proposals is based on empirical evidence amid COVID-19. In terms of social and ecological levels of influence, most of the interventions were developed on the individual level, as opposed to the community level or social level, effectively placing the healthcare responsibility on the victims rather than government and health officials. Furthermore, most of the articles failed to address risks associated with utilizing technology-based interventions (e.g., privacy issues) or navigating the online environment (e.g., cyberstalking). Conclusion: Overall, our findings highlight the need for greater research endeavors on the research topic. Although technology-based interventions have great potential in resolving domestic violence victims’ mental health issues, risks associated with these health solutions should be comprehensively acknowledged and addressed.

Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions

Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning-based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to machine learning-based risk models development and evaluation: (1) biases in the data; (2) clinical documentation system design issues; (3) lack of centralized evidence base for child abuse and neglect; (4) lack of "gold standard "in assessment and diagnosis of child abuse and neglect; (5) challenges in evaluation of risk prediction performance; (6) challenges in testing predictive models in practice; and (7) challenges in presentation of machine learning-based prediction to clinicians and patients. We provide recommended solutions to each of the 7 ethical challenges and identify several areas for further policy and research.

Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study

OBJECTIVE

The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.

MATERIALS AND METHODS

We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect.

RESULTS

Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation.

DISCUSSION

Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect.

CONCLUSIONS

Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.

Knowledge, attitudes, and practices towards artificial intelligence among young pediatricians: A nationwide survey in France

OBJECTIVE

To assess the knowledge, attitudes, and practices (KAP) towards artificial intelligence (AI) among young pediatricians in France.

METHODS

We invited young French pediatricians to participate in an online survey. Invitees were identified through various email listings and social media. We conducted a descriptive analysis and explored whether survey responses varied according to respondents' previous training in AI and level of clinical experience (i.e., residents vs. experienced doctors).

RESULTS

In total, 165 French pediatricians participated in the study (median age 27 years, women 78%, residents 64%). While 90% of participants declared they understood the term "artificial intelligence", only 40% understood the term "deep learning". Most participants expected AI would lead to improvements in healthcare (e.g., better access to healthcare, 80%; diagnostic assistance, 71%), and 86% declared they would favor implementing AI tools in pediatrics. Fifty-nine percent of respondents declared seeing AI as a threat to medical data security and 35% as a threat to the ethical and human dimensions of medicine. Thirty-nine percent of respondents feared losing clinical skills because of AI, and 6% feared losing their job because of AI. Only 5% of respondents had received specific training in AI, while 87% considered implementing such programs would be necessary. Respondents who received training in AI had significantly better knowledge and a higher probability of having encountered AI tools in their medical practice (p < 0.05 for both). There was no statistically significant difference between residents' and experienced doctors' responses.

CONCLUSION

In this survey, most young French pediatricians had favorable views toward AI, but a large proportion expressed concerns regarding the ethical, societal, and professional issues linked with the implementation of AI.