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Leveraging Artificial Intelligence and Natural Language Processing in Legal Epidemiology Studies: Opportunities and
Regen Weber-Fares1, Fallon Julia Cochlin1, Snigdha Peddireddy2
1Health Policy and Management, Texas A&M University School of Public Health, United States.
Summary
Artificial intelligence and natural language processing (AI/NLP) can accelerate legal epidemiology, the study of law's health effects. This review outlines methods for using AI/NLP to overcome specialist shortages and advance evidence-based policy.
Area of Science:
- Legal epidemiology
- Public health policy
- Health law
Background:
- Legal epidemiology examines law's impact on health outcomes.
- Progress is limited by a shortage of specialists for time-intensive analyses.
- Laws possess characteristics suitable for AI/NLP due to standardization and defined terminology.
Purpose of the Study:
- To review opportunities and challenges of AI/NLP in scientific legal research.
- To address concerns regarding valid AI/NLP application in policy evaluation.
- To propose a method-focused research agenda for AI/NLP in legal epidemiology.
Main Methods:
- Review of recent literature and case deployments of AI/NLP in legal research.
- Assessment of AI/NLP applications for legal document scope, data collection, and coding schemes.
- Emphasis on quality control for valid legal datasets.
Main Results:
- AI/NLP offers solutions to specialist shortages in legal epidemiology.
- Identified opportunities include data scope assessment, collection, and coding.
- Challenges involve ensuring validity and addressing underreporting in policy evaluations.
Conclusions:
- AI/NLP methods can significantly advance scientific legal epidemiology.
- Methodologic innovation and reporting are crucial for the field's growth.
- This review provides a research agenda to accelerate AI/NLP's impact on evidence-based policy.
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