This repository is dedicated to advancing the development and evaluation of Large Language Models (LLMs) for pediatric growth assessment. Monitoring physical growth and developmental milestones is a cornerstone of pediatric healthcare, crucial for early identification of potential health issues and guiding timely interventions. This project aims to address the critical gap in specialized benchmark datasets required to train and validate LLMs for these sensitive and vital tasks.
Our goal is to foster the creation and utilization of robust, ethically sourced, and clinically relevant datasets that can drive innovation in AI-powered pediatric care.
While LLMs show immense promise across various domains, their application in pediatric growth assessment is currently hindered by a significant scarcity of appropriate data. Existing general medical datasets (e.g., MIMIC-IV, PEDSnet) and standard growth charts (CDC/WHO) offer foundational information but often lack the granularity, pediatric-specific focus, longitudinal depth, and multimodal richness required for sophisticated LLM applications.
Key challenges with current data resources include:
- Lack of Pediatric Specificity: Insufficient detailed data points unique to child growth and development.
- Limited Longitudinal Data: Difficulty in tracking growth patterns and trajectories over extended periods of a child's development.
- Absence of Multimodal Integration: Need for datasets that combine clinical notes, growth measurements, imaging data, genetic information, and socio-environmental factors.
- Risk of Bias: Potential for age-related, demographic, or socio-economic biases if datasets are not carefully curated.
This initiative aims to:
- Define Standards: Outline the characteristics of an ideal benchmark dataset for pediatric growth assessment using LLMs.
- Highlight Gaps: Identify and analyze the limitations of existing datasets for this specific application.
- Promote Best Practices: Advocate for ethical considerations, including data privacy, informed consent, and bias mitigation in dataset development and use.
- Encourage Collaboration: Foster a community of researchers, clinicians, and AI developers to contribute to the creation and refinement of such benchmarks.
- Facilitate Evaluation: Provide a framework or point of reference for evaluating LLMs on tasks related to pediatric growth.
Based on our analysis, an effective benchmark dataset for LLM-driven pediatric growth assessment should possess the following attributes:
- Scale and Diversity:
- Large-scale: Sufficient volume of data to train robust models.
- Diverse: Representation across various demographics (age, sex, ethnicity), geographic locations, socioeconomic backgrounds, and health conditions.
- Data Richness:
- Longitudinal: Comprehensive records tracking individual growth trajectories over time, from infancy through adolescence.
- Multimodal: Integration of various data types, including:
- Standard growth parameters (height, weight, head circumference).
- Clinical notes and narratives.
- Developmental milestones.
- Laboratory results.
- Imaging data (e.g., bone age X-rays).
- Genetic information.
- Nutritional data.
- Socio-environmental factors.
- Quality and Annotation:
- High-Quality Data: Accurate, consistent, and validated data points.
- Expert Annotation: Clinically relevant labels and annotations for specific growth patterns, conditions, or outcomes, performed by pediatric specialists.
- Ethical Framework:
- Privacy-Preserving: Robust de-identification techniques and adherence to data privacy regulations (e.g., HIPAA, GDPR).
- Informed Consent: Clear protocols for obtaining informed consent from parents/guardians for data use in research.
- Bias Mitigation: Proactive measures to identify and address potential biases within the dataset.
- Data Governance: Transparent policies for data access, sharing, and usage.
High-quality benchmark datasets will enable the development and validation of LLMs for a range of impactful applications in pediatric growth assessment, including:
- Automated Growth Chart Interpretation: Identifying deviations from normal growth patterns and flagging children at risk.
- Early Prediction of Growth Disorders: Forecasting conditions like failure to thrive, short stature, obesity, or endocrine disorders.
- Personalized Growth Trajectories: Developing individualized growth predictions and intervention strategies.
- Enhanced Clinical Decision Support: Providing clinicians with insights derived from large-scale population data.
- Improved Patient-Provider Communication: Generating understandable summaries and explanations of growth assessments for families.
- Identification of Rare Conditions: Assisting in the diagnosis of rare genetic or metabolic disorders affecting growth.
This repository currently serves as a central point for information and discussion based on analyses of existing literature and model capabilities concerning pediatric growth data.
We invite researchers, clinicians, data scientists, and healthcare organizations to:
- Contribute Insights: Share knowledge about existing (even private) pediatric datasets that could inform benchmark creation.
- Discuss Methodologies: Engage in discussions about data collection, anonymization, annotation, and ethical best practices.
- Collaborate on Development: Participate in efforts to curate, standardize, and potentially share de-identified pediatric growth data for benchmarking purposes.
- Propose Evaluation Tasks: Suggest specific tasks and metrics for evaluating LLMs in this domain.
(Further sections could be added here as the project evolves, e.g., "Benchmark Tasks," "Evaluation Metrics," "Accessing the Benchmark," "Leaderboard.")
The development and use of LLMs in pediatric healthcare, particularly with sensitive growth data, demand the highest ethical standards. All efforts related to this benchmark must prioritize:
- Child Welfare: Ensuring that any application ultimately benefits child health and well-being.
- Data Security and Privacy: Implementing state-of-the-art security measures and de-identification protocols.
- Transparency and Explainability: Striving for LLM outputs that are understandable and justifiable to clinicians and families.
- Fairness and Equity: Actively working to prevent the perpetuation or amplification of biases.
The documentation and any code provided in this repository are licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
(Note: If this repository were to include or directly link to datasets, the license for the data itself would need separate and careful consideration, potentially involving Data Use Agreements (DUAs) and differing from the software/documentation license.)
If you use insights or frameworks from this project, please consider citing this repository (details to be added once a formal publication or preprint is available).
For questions, suggestions, or collaboration inquiries, please open an issue in this repository or contact Joon Lee ([email protected]).