How AI is Changing Finance, Healthcare, and Law
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
By
Zhiyu Zoey Chen|Jing Ma|Xinlu Zhang|Nan Hao|An Yan|Armineh Nourbakhsh|Xianjun Yang|Julian McAuley|Linda Petzold|William Yang Wang

https://arxiv.org/abs/2405.01769v2
Summary
Imagine a world where diagnosing illnesses, navigating complex financial markets, and understanding dense legal documents are made significantly easier with the help of artificial intelligence. That's the promise of Large Language Models (LLMs), sophisticated AI systems capable of understanding and generating human-like text. A recent research paper explores how these powerful tools are poised to revolutionize critical sectors like finance, healthcare, and law. LLMs are not just automating tasks; they're transforming how professionals in these fields work. In healthcare, LLMs can analyze medical images and patient records, assisting doctors in making faster, more accurate diagnoses and personalized treatment plans. Imagine an AI that can quickly sift through mountains of medical research to find the most relevant information for a patient's specific condition. This is the power of LLMs in healthcare. In finance, LLMs can analyze market trends, assess risks, and even generate financial reports, providing analysts with deeper insights and more efficient tools. Think of an AI that can instantly summarize complex financial news and predict market movements with greater accuracy. This is the potential of LLMs in finance. In law, LLMs can analyze legal documents, predict case outcomes, and even assist in drafting contracts, making legal processes more efficient and accessible. Imagine an AI that can quickly find relevant case law and summarize complex legal jargon, making legal information more understandable. This is the transformative power of LLMs in law. However, the rise of LLMs also presents challenges. Ensuring these AI systems are fair, unbiased, and transparent is crucial, especially in fields where decisions have significant consequences. Researchers are actively working on addressing these ethical concerns, developing methods to make LLMs more robust and reliable. The future of LLMs in these critical sectors is bright, promising to empower professionals and improve services in ways we're only beginning to imagine. As these technologies continue to develop, we can expect even more innovative applications that will reshape how we interact with finance, healthcare, and the legal system.
🍰 Interesting in building your own agents?
PromptLayer provides the tools to manage and monitor prompts with your whole team.
Get started for free.Question & Answers
How do Large Language Models analyze medical images and patient records to assist in diagnosis?
Large Language Models (LLMs) process medical data through a multi-step analysis process. First, they use pattern recognition to identify anomalies in medical images and cross-reference these findings with extensive medical databases. Then, they analyze patient records by extracting relevant information about symptoms, medical history, and test results. For example, an LLM might analyze a chest X-ray alongside a patient's historical records, current symptoms, and relevant medical literature to suggest potential diagnoses. This integration of multiple data sources helps doctors make more informed decisions while reducing the time needed for comprehensive analysis.
What are the main benefits of AI in financial decision-making?
AI brings three major advantages to financial decision-making. First, it can process vast amounts of market data in real-time, identifying patterns and trends that humans might miss. Second, it provides more accurate risk assessment by analyzing multiple variables simultaneously, helping investors and institutions make better-informed decisions. Third, it automates routine financial tasks like report generation and basic analysis, freeing up professionals to focus on strategic decisions. For example, AI can automatically monitor market movements, alert users to potential investment opportunities, and generate comprehensive financial reports in minutes rather than hours.
How is artificial intelligence making legal services more accessible to the public?
AI is democratizing legal services in several ways. It makes legal information more understandable by translating complex legal jargon into plain language, helping people better understand their rights and obligations. AI-powered tools can help individuals find relevant legal documents and precedents quickly, reducing the time and cost of legal research. Additionally, automated document analysis and contract review services make basic legal services more affordable for small businesses and individuals. For instance, AI can help people create basic legal documents, understand contract terms, or find relevant laws for their specific situation without requiring expensive legal consultation.
.png)
PromptLayer Features
- Testing & Evaluation
- Critical for validating LLM outputs across regulated industries like healthcare and finance where accuracy and compliance are essential
Implementation Details
1. Create domain-specific test sets for each industry 2. Implement automated accuracy checks against expert-validated data 3. Set up continuous monitoring of model outputs
Key Benefits
• Ensures regulatory compliance
• Maintains high accuracy standards
• Enables systematic performance tracking
Potential Improvements
• Industry-specific evaluation metrics
• Automated bias detection
• Real-time performance alerts
Business Value
.svg)
Efficiency Gains
Reduces manual validation time by 70%
.svg)
Cost Savings
Minimizes risk of costly errors in regulated environments
.svg)
Quality Improvement
Ensures consistent output quality across different use cases
- Analytics
- Workflow Management
- Enables structured implementation of multi-step LLM processes across different professional domains
Implementation Details
1. Design domain-specific workflow templates 2. Implement version control for each step 3. Create reusable components for common tasks
Key Benefits
• Standardized processes across teams
• Traceable decision-making
• Reproducible results
Potential Improvements
• Domain-specific templates
• Enhanced error handling
• Automated workflow optimization
Business Value
.svg)
Efficiency Gains
Streamlines complex processes by 40%
.svg)
Cost Savings
Reduces operational overhead through automation
.svg)
Quality Improvement
Ensures consistent application of best practices