Transforming Pediatric Care: How AI Supports Treatment and Diagnosis
Introduction to AI in Pediatric Care.
Artificial Intelligence (AI) is becoming an increasingly powerful tool in healthcare. In simple terms, AI refers to computer systems that can perform tasks that normally require human intelligence such as recognizing patterns, learning from data, and making predictions. In the healthcare field, AI is used to support diagnosis, track patient health, recommend treatments, and even assist in surgery. AI in healthcare is not just one single tool; it is made up of different types of technologies, each with specific strengths. The most common types used in pediatric treatment are Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). They all interconnect when a task needs to be finished as shown in Figure 1.
Figure 1: Interconnectedness of AI (Han et al. 2024)
ML refers to algorithms that can learn from patterns in data such as patient records, test results, or symptom histories. DL uses layered neural networks that can recognize subtle patterns often missed by human eyes or simpler methods by using X-Rays, CT scans, MRI’s, etc. NLP uses computers to understand and interpret human language. Each plays a different but important role in helping doctors diagnose conditions more accurately, predict health risks, and personalize treatment plans for young patients. By doing so, AI helps clinicians make faster, more accurate diagnoses, predict health risks, and personalize treatment plans, all while supporting timely interventions that are crucial for a child’s long-term health and development. In essence, AI acts as a powerful assistant, helping pediatricians navigate the complexities of treating developing bodies, unpredictable symptoms, and diverse pediatric conditions.
2. Use of AI in Pediatric Treatments.
2.1 Machine Learning in Pediatric Treatments.
Machine Learning (ML) uses the information from patient records and test results to make predictions or decisions. It is said that “ML offers promising solutions to address challenges by improving diagnostic accuracy, enhancing treatment strategies, and predicting outcomes tailored to pediatric patients” (Ganatra). These systems continuously improve as they process more data, making them a valuable tool for pediatricians aiming to intervene early and personalize care. Another promising use of machine learning in pediatric treatments is its ability to support personalized medicine. Children respond to medications and therapies in different ways due to variations in their genetic makeup, age, and developmental stage. Traditional treatment approaches often follow generalized protocols that may not account for these differences. In a research study done in Saudi Arabia by Sami Al-Hajjar, she found that AI achieved radiologist-level accuracy, predicted critical conditions (sepsis, hypoxemia), and optimized pediatric treatment plans with genomic. In this study it demonstrated, “machine learning algorithms can identify genetic predispositions, enabling treatments tailored to the child’s unique genetic profile” (Sami Al-Hajjar, 2024). By analyzing large-scale genomic data, ML models can detect mutations or inherited risk factors that influence how a child may respond to certain drugs or therapies. For instance, in pediatric oncology, machine learning tools are helping doctors personalize cancer treatments based on a child’s tumor genetics. In a research article done by researchers in Sichuan, China, they said, “…it’s warranted urgently to develop dedicated AI algorithms applied in pediatric oncology” (Yang et al., 2023). They found that using AI in pediatric oncology is very helpful, but current tools (often built for adults) aren’t enough. Therefore, they strongly agreed that we urgently need AI models built just for pediatric cancer care. As these algorithms became more refined and pediatric genomic databases grow, machine learning has the potential to revolutionize how treatments are selected; moving from one-size-fits-all to highly individualized care for young patients.
2.2 Deep Learning in Pediatric Treatments.
Deep learning is also making strides in understanding the neurological basis of developmental disorders like Autism Spectrum Disorder (ASD). One notable application is in the use of advances of DL throughout the years to detect early signs of ASD in children (Figure 2).
Figure 2:
ASD research advancements (Farhat et al. 2025)
In a study conducted by Wang and colleagues, the authors observed that “In recent years, the application of deep learning techniques to neuroimaging methods has demonstrated significant potential in advancing etiological research on Autism Spectrum Disorder (ASD) in preschool-aged children” (Wang et al., 2023). Further proving their statement, a study by Iidaka et al. exemplifies this: “researchers collected resting-state fMRI data from 642 adolescents under 20 and used deep learning—in the form of a probabilistic neural network—to classify subjects based on brain activity” (Iidaka et al, 2015). Impressively, their model achieved around 90% accuracy, and the results remained consistent regardless of how the data was grouped or which institution it came from. This kind of research shows how deep learning can contribute not only to early detection but also to a better understanding of how ASD develops in the brain. In the context of pediatric disease prevention, one study highlights that “in addition to model accuracy, the earlier a model can identify the disease risk, the better the opportunities for intervention will be” (Javidi et al., 2024), underscoring how critical timing is when treating young patients. This reinforces the value of integrating AI models, particularly those using deep learning and predictive analytics into pediatric care, not just for their precision but for their ability to act early. As these technologies continue to evolve, early detection powered by AI has the potential to shift pediatric care from reactive treatment to proactive prevention.
2.3 Natural Language Processing in Pediatric Treatments.
One of Natural Language Processing (NLP) most valuable uses in analyzing electronic health records (EHRs), which often contain large amounts of unstructured data like physician notes, symptom descriptions, or lab observations. NLP tools can quickly scan and extract relevant information from these records, helping doctors identify trends or missed details that could affect a child’s diagnosis or treatment. For example, if a physician writes in their notes that a child has been “coughing for over two weeks with worsening fatigue,” an NLP system can flag this as a potential concern and bring it to a clinician’s attention for further review. NLP is also being used to power virtual assistants and symptom checkers that guide parents through decision-making, especially in rural settings where access to care may be limited. As said by researcher Fliesler, “aided by machine-learning algorithms, these systems are capturing problems that clinicians didn’t explicitly flag through a safety reporting system, but documented in their notes” (Fliesler et al., 2019). These tools can ask follow-up questions, suggest whether medical attention is needed, and even provide basic health education. As NLP models become more refined and better trained on pediatric-specific language and cases, their ability to support faster, safer, and more informed decisions in child healthcare will continue to grow.
Beyond streamlining health records and powering virtual assistants, NLP is also transforming how researchers and pediatric specialists study large-scale health data. For instance, one study applied NLP to pediatric clinical notes and found a much higher rate of identified child maltreatment compared to using ICD codes alone, demonstrating NLP’s power to unlock critical insights hidden in unstructured texts (Negriff et al., 2023). This finding is important because it shows how much valuable information can be overlooked when pediatric research relies only on traditional diagnostic coding systems. By tapping into the unstructured notes that doctors actually write, NLP provides a fuller picture of a child’s health and circumstances. For pediatric specialists, this means having better tools to recognize patterns, intervene earlier, and design more effective treatments. On a larger scale, it also helps researchers gain a clearer understanding of trends across populations of children, which can shape public health strategies and policies. Ultimately, studies like this highlight how NLP isn’t just a convenience; it’s a transformative tool that makes pediatric care and research more accurate and responsive to children’s real needs.
3. Limitations of the Use of AI in Pediatric Care
While AI technologies like NLP and machine learning are showing great promise in pediatrics, it is important to acknowledge their limitations. One concern is that algorithms are only as reliable as the data they are trained on. Clinical notes and medical records can contain biases, incomplete information, or even errors, which may then be reflected in the AI’s predictions. As some researchers point out, “AI has the potential to cause harm and therefore requires serious clinical consideration and risk identification and mitigation as part of any workflow implementation process (Bhargava et al., 2024). While it is true that AI carries the potential for harm if implemented without proper oversight, this concern highlights the importance of responsible and carefully monitored integration rather than rejecting the technology. By incorporating rigorous risk assessment, validation processes, and ongoing clinician supervision, AI tools can be safely used to support pediatric care. In fact, when these safeguards are in place, AI can enhance decision-making, reduce errors, and improve early detection of diseases, ultimately benefiting both children and healthcare providers. Recognizing potential risks encourages thoughtful deployment and ethical use, ensuring that AI serves as a complementary tool rather than a replacement for human judgment in pediatric healthcare.
Experts caution that AI tools designed for adults may not always translate well to pediatric patients. As Bourgeois explains, “For example, a radiology device that screens head CTs and flags those of particular concern may not fit kids, whose anatomy or disease processes are different” (Fliesler 2024). This example highlights the critical need for pediatric-specific AI development and careful validation before clinical use. Children’s anatomy and disease processes differ from adults, meaning AI algorithms trained on adult data may misinterpret pediatric cases, leading to misdiagnoses or missed conditions. By recognizing these limitations, developers and clinicians can take proactive steps; such as creating models trained on pediatric datasets, conducting thorough testing, and implementing ongoing monitoring to ensure AI tools are safe and effective for children. Rather than discouraging the use of AI in pediatrics, these considerations reinforce the importance of responsible design and deployment, allowing AI to complement human expertise and improve care without compromising safety.
While many AI frameworks articulate high-level ethical principles, ethical guidelines alone may not be enough to ensure responsible practices. As Brent Mittelstadt argues, “significant differences exist between medicine and AI development that suggest a principled approach in the latter may not enjoy success comparable to the former” (Mittelstadt). This perspective is especially important in pediatrics because children represent a uniquely vulnerable population. If ethical guidelines for AI remain too broad or unenforceable, pediatricians may face tools that are not adequately tested for young patients or that fail to account for their medical needs (Figure 3).
Figure 3:
Interconnection of ethical principles ensuring trust in child-centered medical AI (Seo Yi Chng et al. 2025)
Unlike adult populations, where more data exists, pediatric AI models often rely on smaller datasets, which increases the risk of bias and error. Mittelstadt’s point highlights that without strong oversight and accountability; like the standards medicine already enforces; AI tools in pediatrics may create more risks than benefits. Therefore, moving beyond abstract principles toward concrete safeguards and regulations is essential to ensure children’s safety.
While concerns about bias, ethical oversight, and the risk of misapplication are valid, they do not diminish the potential of artificial intelligence to transform pediatric care. The evidence shows that when implemented responsibly, AI can support earlier diagnoses, personalize treatments, and give clinicians new insights into complex conditions. Rather than viewing limitations as barriers, they should be seen as guideposts for how to build safer, more transparent systems. Children stand to benefit enormously from advances in machine learning, deep learning, and natural language processing, as long as these tools are carefully adapted to their unique needs. Ultimately, AI is not a replacement for pediatricians, but with the right safeguards, it can help deliver faster, more accurate, and more compassionate care to the youngest patients.
4. Conclusion
Artificial intelligence is steadily reshaping pediatric medicine, offering solutions to challenges that have long constrained clinicians, researchers, and families. Throughout this paper, ML, DL, and NLP AI can improve the outcomes of pediatric treatments. A study in the Journal of Pediatric Psychology revealed that “parents seeking health care information for their children often trusted AI more than health professionals…” and even rated AI-generated text as “credible, moral and trustworthy” (The University of Kansas, 2024). This finding suggests that despite potential risks, families may be open to AI’s role in healthcare, particularly when it is presented responsibly and transparently.
Looking ahead, the promise of AI in pediatrics is not simply in its technical capabilities but in how it can complement human expertise. Pediatricians bring compassion, intuition, and the ability to connect with children and families in ways machines never can. AI, on the other hand, offers speed, precision, and the ability to process vast amounts of information beyond human capacity. Together, they create a powerful partnership; where AI provides insights and predictions, and pediatricians apply their judgment to deliver care that is both accurate and empathetic. Children deserve the best possible start in life, and artificial intelligence, when responsibly integrated, can be one of the most powerful tools to help achieve that goal.
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