This is a cross-sectional online study using several regional (i.e., Ile de France, the Paris area) and national mailing lists and social media. We aimed at including young pediatricians registered on the Facebook groups “Internes de France” (n = 17,096), “Association des Juniors en Pédiatrie” (AJP; n = 1,267), “Promo 2019 de Pédiatrie” (n = 87), “Pédiatrie Paris Promo socle” (n = 88), “Pédiatrie Paris Promo 2020” (n = 93) as well as on AJP-Paris’ mailing lists, regrouping Ile-de-France pediatrics residents from 2016 to 2021 (n = 465; Appendix 1). The survey was anonymous. Only pediatricians were eligible; young physicians from other specialties were excluded. There were no strict age limits, but most social media groups we used were targeted at residents and fellows. AJP's mailing list includes young pediatricians, mostly below 30 years old, all below 40. Data collection was conducted from January 12 to February 16, 2022. We followed the CHERRIES statement for reporting (Appendix 2).

The online questionnaire was developed and administered via Google Form. Consent, age, sex, faculty of origin, level of experience, and any additional training were gathered from the initial seven questions. The survey was then structured into four parts: (1) knowledge about AI (5 questions), (2) expected benefits of AI (13 questions), (3) fears toward AI (8 questions), and (4) practices regarding AI (6 questions). Responses to closed questions were collected on a 5-point Likert scale (i.e., “totally disagree”, “rather disagree”, “neutral”, “rather agree”, “totally agree”). Answers to questions expecting a numerical entry were offered a range of plausible values. Each social media and mailing list received a separate questionnaire, and the response files were merged for analysis. There was no need to answer all the questions of the survey to be included in the study, and partial responses were kept in the analysis. Because questionnaires were anonymous, it was not possible to detect and exclude duplicates, but we believe it is very unlikely that respondents took the survey several times.

The “residents” category included residents from the first to the eighth semester of residency training. The “experienced doctors” category included chief residents (“Docteur Junior” status), physicians working under a resident contract (“Faisant Fonction d’Interne” status), fellows (“Assistant/Chef de Clinique” status), attending physicians, professors (assistant, associate, and full), and private practitioners. We considered the responses “totally agree” and “rather agree” as positive, and the responses “rather disagree” and “totally disagree” as negative. Neutral responses were considered a third response category.

We first performed a descriptive analysis of study participants and survey responses. Descriptive statistics included means and medians for qualitative variables. Survey responses were summarized as percentages. In an exploratory approach, we used Chi-square tests and Fisher's exact tests (if n < 5) to compare proportions and assess if responses varied according to whether respondents had received (specific or non-specific) training in AI. We also compared residents’ and experienced doctors’ responses. All analyses involved the use of R software (R Foundation, Austria, Vienna). The significance threshold was set at 0.05. There was no specific sample size calculation for this survey.

Participation in the survey was voluntary. A short paragraph was included at the beginning of the questionnaire to inform participants of the study's objectives and of the confidentiality of their responses. Consent was considered obtained by virtue of questionnaire completion. Data were collected anonymously, and participants had the right to access and cancel their answers. In accordance with French legal regulations, ethical approval was not required for this study.

One hundred and sixty-five pediatricians responded to the survey (Figure 1) The participation rate was difficult to estimate due to potential redundancies across social media groups and mailing lists. Respondents’ median age was 27 years (interquartile range 25–30 years), and 78% of respondents were women. In total, 75% of the participants attended medical school in Ile-de-France (Paris V, Paris VI, Paris VII, UPEC, Paris-Sud, Paris 13, Versailles-Saint-Quentin Universities). Regarding clinical experience, 64% of respondents were classified as residents, while 36% of included pediatricians were “experienced doctors”.

In total, 90% of the participants stated that they understood the term “artificial intelligence”, compared to 65% for “machine learning”, 54% for “neural network” and only 40% for “deep learning”. Only 31% of the participants reported that they knew the difference between these different terms, while half of them (49%) did not. From a regulatory perspective, 58% of the respondents declared they were familiar with the General Data Protection Regulation (GDPR) framework.

The majority of the surveyed pediatricians seemed to have a favorable view of AI (Table 1). The participants appeared to think that AI could lead to major improvements in medicine, particularly in terms of medical training (88%), better access to healthcare in settings where experts are not available (80%), diagnostic assistance (71%), better compliance with treatment and patient follow-up (91%), and help for choosing among various therapeutic options (73%). More than half (60%) of respondents agreed with the statement that AI would “revolutionize medicine” while a quarter (24%) preferred to stay neutral on this question. A majority (86%) declared that they would favor the implementation of AI tools in pediatrics.

More than half of respondents (59%) saw AI as a threat to medical data security and 35% as a threat to the ethical and human dimensions of medicine (Table 2). Regarding their practice of medicine, 39% feared skill loss induced by task delegation to AI, but only 6% of the pediatricians stated that they feared losing their job because of AI.

Only 5% of the pediatricians stated that they had received specific teaching in AI, and 29% of pediatricians declared having been in contact with AI through specific or non-specific training (Table 3). In total, 42% of the participants declared that they had encountered at least one AI tool in their clinical practice. The vast majority of respondents considered that it would be necessary to implement specific training and courses about AI (87%).

Forty-eight physicians (29%) declared they had contact with AI through (specific or non-specific) training. These respondents significantly more often stated: (i) to know the difference between the terms “artificial intelligence”, “machine learning”, “neural network”, and “deep learning” (44% vs. 26%, p = 0.027), (ii) to know what the General Data Protection Regulation (GDPR) is (73% vs. 51%, p = 0.009), and (iii) to have encountered AI tools in their medical practice (65% vs. 33%, p < 0.001). We found no statistically significant difference between residents’ and experienced doctors’ responses (Appendix 3).

In this nationwide online survey of 165 young French pediatricians, we assessed their KAP towards AI in healthcare. We found insufficient knowledge in the lexical field and core concepts of AI, as, for example, 49% of the participants did not know the differences between the terms “artificial intelligence”, “machine learning”, “neural network” and “deep learning”. We also observed that the majority of respondents seemed to have a positive view of AI and were in favor of implementing AI tools in pediatrics. In contrast, more than half of respondents saw AI as a threat to medical data security and one-third as a threat to the ethical and human dimensions of medicine. Forty-two percent of the participants declared that they had encountered at least one AI tool in their medical practice, whereas only 5% of the pediatricians stated that they had received specific teaching about AI in medical school. Respondents who received training in AI had significantly better knowledge and a higher probability of encountering AI tools in their medical practice (Appendix 3).

To our knowledge, this is the first study focusing on the KAP towards AI among pediatricians in France. Similar studies have been conducted in other fields of medicine, including radiology (15, 16), psychiatry (17), and dermatology (18), but not in pediatrics, while several AI applications are being implemented in this specialty (3, 12).

Our finding that less than a third (31%) of pediatricians knew the difference between “artificial intelligence”, “machine learning”, “neural network”, and “deep learning” is in line with other studies that also questioned physicians on their understanding of AI [response rates: 24% among dermatologists (18) and 35% among radiologists (16)]. Only 5% of the pediatricians in our survey said they had received specific training in AI, but 42% had encountered AI solutions in their practice. In the study by Ooi et al. among radiologists (16), a similar proportion of only 5% of respondents stated that they had received training in AI. Still, the majority of them commonly used AI solutions in their practice (93% for voice recognition and 56% for image interpretation) (16). It appears that specific training on AI in medical studies is currently lacking. Hence, doctors may confront AI tools directly in their clinical practice without prior instruction about the concepts behind algorithms, how AI solutions are developed and evaluated, and their limitations and potential biases (19, 20). Accordingly, 87% of the pediatricians in our survey considered that it would be necessary to offer specific training in AI, as 80% of the dermatologists surveyed by Polesie et al. (18) and 89% of the radiologists in the Ooi study (16).

Regarding their attitudes toward AI in medicine, 60% of young French pediatricians believed that AI would “revolutionize medicine”. A greater proportion (89%) of radiologists surveyed in the Ooi study agreed that AI would “revolutionize the practice of their specialty” (16). This higher proportion could be because the vast majority (93%) of radiologists already commonly used AI solutions in their practice (16). In addition, radiology relies on advanced technologies, whereas pediatricians might be more focused on clinical skills. In the study surveying dermatologists (18), 69% of respondents believed that AI would “revolutionize dermatology”. However, the authors explained that “dermatologists with a special interest in dermatoscopy were more likely to have received the invitation [to participate in the survey]”, potentially shifting the results toward a positive perception of AI.

Regarding the potential negative attitudes toward AI, only 6% of pediatricians expressed their fear of losing their job because of AI. This low proportion was also found in dermatology (5.4%) (18). Also, in psychiatry, only 3.8% of responding physicians feared that AI would make their practice obsolete, but as high as 75% of them thought that AI could replace them in completing and updating medical records (17). Coppola's study among 1,032 radiologists reported that 11% of them were afraid of being replaced by an AI solution (15). This shows that most physicians across several specialties see AI as having the potential to help clinicians rather than replace them.

Attitudes towards AI were generally positive: 86% of French pediatricians in our survey favored implementing AI tools in their specialty, as were 77% of radiologists in Coppola's study (15). When asked whether increased use of AI would make their specialty more “exciting”, 63% of dermatologists (18) and 76% of radiologists agreed (16). AI thus seems generally perceived as a solution to enhance clinical practice.

Our study has limitations. First, there was probably a selection bias due to our distribution channels favoring access to young pediatricians trained in the Paris region (75%). This population of young pediatricians might be more exposed to research and innovation, including AI, in their training and clinical practice than in other regions. Furthermore, determining the exact response rate was not practicable because of the potential for user overlap between groups. Given that in 2022, France counted 1,635 residents training in Pediatrics, our best approximation of the response rate in this subgroup is around 6% (105/1,635). Hence, our collected responses may not represent the KAP of all young French pediatricians. Second, we opted for a relatively short and shallow questionnaire to maximize the completion rate. Qualitative and mixed-methods studies are needed to allow deeper investigations of barriers and facilitators of AI use in pediatrics.