Case Studies

Generative AI is increasingly being adopted in the legal industry to automate various tasks and enhance efficiency. Here are some case studies highlighting the use of generative AI in legal applications:

Case Study 1: Contract Review and Analysis

Law firms and corporate legal departments are using generative AI tools like CoCounsel and Harvey to review and analyze contracts. These AI assistants can quickly scan through lengthy contracts, identify key clauses, potential issues, and provide summaries, saving significant time and effort for lawyers.

For example, a lawyer at a large law firm used CoCounsel to review a 100-page contract for a client. The AI assistant generated a concise 2-page summary highlighting essential clauses, obligations, potential risks, and relevant legal precedents in just a few minutes. This allowed the lawyer to quickly grasp the key points and focus on strategic analysis rather than spending hours manually reviewing the document.

Case Study 2: Legal Document Drafting

Generative AI can assist in drafting various legal documents like contracts, pleadings, and wills by using pre-defined templates and user inputs. Law firms are leveraging AI tools like LawGeex to automate the drafting process, ensuring consistency and reducing errors.

For instance, a corporate legal team used an AI drafting tool to generate a non-disclosure agreement (NDA) for a new business partnership. The lawyer provided key details like party names, confidential information, and term duration, and the AI generated a customized NDA draft in minutes. The lawyer then reviewed and finalized the document, saving significant time compared to drafting from scratch.

Case Study 3: Legal Research and Analysis

Generative AI tools can expedite legal research by quickly searching and analyzing relevant case law, legislation, and secondary sources. Law firms are using AI assistants like CoCounsel and Harvey to conduct comprehensive legal research and identify pertinent information efficiently.

A litigation team at a law firm used an AI research assistant to gather and analyze relevant case law for a complex legal dispute. The AI scanned through thousands of cases, identified the most relevant ones, and provided summaries of key legal arguments, court analyses, and precedents. This allowed the lawyers to quickly grasp the legal landscape and develop a strong strategy for the case.

These case studies demonstrate how generative AI is transforming various legal tasks, enabling lawyers to work more efficiently, reduce manual effort, and focus on higher-value strategic work. As the technology continues to evolve, its adoption in the legal industry is expected to grow, driving innovation and enhancing the delivery of legal services.

Case studies on using generative AI in education:

Case Study 1: Generating Lesson Plans and Course Materials

Many educators are using generative AI tools like ChatGPT to assist in creating lesson plans, course outlines, and supplementary learning materials. This can save significant time compared to creating these resources from scratch.

For example, a high school science teacher used ChatGPT to generate a detailed unit plan on photosynthesis, including lesson objectives, key talking points, experiment ideas, and sample assessment questions. The AI-generated plan provided a solid framework that the teacher could then review, modify, and supplement with their own expertise and materials.

Similarly, a university professor teaching an introductory psychology course used AI to generate draft lecture slides, reading summaries, and practice quiz questions. This allowed the professor to rapidly develop content for the course while still applying their pedagogical knowledge to refine and tailor the AI-generated materials.

Case Study 2: Simplifying Complex Topics

Generative AI can help explain complex topics in simpler terms tailored to a student's grade level or prior knowledge. This supports differentiated instruction and scaffolding learning.

An elementary school teacher used an AI tool to generate age-appropriate explanations of concepts like photosynthesis, gravity, and the water cycle. The AI broke down the ideas into simpler language and relatable examples that young students could better understand.

At the university level, an engineering professor asked ChatGPT to provide simplified summaries of advanced topics like semiconductor physics for students who were struggling. The AI distilled the key points into more digestible chunks without omitting important nuances.

Case Study 3: Interactive Role-Playing Activities

Some educators are experimenting with using generative AI for interactive role-playing case studies that adapt based on student responses, creating a unique learning experience.

In a business ethics course, students engaged with an AI-driven scenario where they played decision-makers at a company facing an ethical dilemma. Based on their choices at each branch point, the AI generated new storylines and consequences, allowing students to explore multiple perspectives dynamically.[1]

For a medical training program, an AI tool presented an interactive virtual patient case where the patient symptoms and test results changed depending on how students responded. This better prepared students for the ambiguity and critical thinking required in real clinical situations.[1]

Case Study 4: Generating Practice Questions and Feedback

Generative AI allows teachers to rapidly create practice questions, quizzes, and worked examples across disciplines, along with tailored feedback and explanations.

An elementary math teacher used an AI tool to generate hundreds of unique practice word problems at varying difficulty levels for students to solve. The AI could also provide step-by-step worked solutions as feedback.

A law school professor had an AI generate sample essays and legal briefs in response to hypothetical cases, which students could then critique and compare to model answers. The AI could also provide personalized feedback highlighting strengths and areas for improvement in students' written work.

While generative AI has immense potential in education, these case studies demonstrate how its role is as an assistive tool to enhance and streamline instructional tasks, not to fully automate or replace human educators. Proper oversight, fact-checking, and pedagogical guidance from teachers remains crucial when incorporating AI into learning activities.

Case studies highlighting the use of generative AI in healthcare:

Case Study 1: Medical Imaging Analysis

Generative AI models are being leveraged to assist radiologists in accurately and quickly analyzing medical images like MRIs, CT scans, and X-rays for diagnosis. These AI models, trained on diverse patient data, can identify early signs of various conditions such as cancers, Alzheimer's disease, and diabetic retinopathy, uncover biomarkers indicative of specific disorders, and predict disease progression.[3]

For instance, researchers introduced a pancreatic cancer detection technology called PANDA, which uses generative AI to analyze CT scans and identify potential pancreatic tumors with high accuracy. This technology can speed up the diagnostic process and improve early detection, leading to better patient outcomes.

Case Study 2: Virtual Assistants for Patient Care

Healthcare providers are exploring the use of generative AI-powered virtual assistants to interact with patients, answer their queries, and provide personalized health advice. These AI assistants can understand natural language, analyze patient data, and generate tailored responses, improving patient engagement and access to care.

For example, Northwell Health, a healthcare provider in New York, has implemented an AI virtual assistant called "Northy" to assist patients with scheduling appointments, answering medication-related questions, and providing general health information. Northy can converse in multiple languages, making healthcare more accessible to diverse patient populations.

Case Study 3: Personalized Medication and Treatment Plans

Generative AI excels at analyzing complex and diverse information, making it well-suited for developing personalized medication regimens and treatment plans based on individual patient data, including genetic profiles, medical history, and real-time health data from wearable devices.

Researchers at Stanford University have developed a generative AI system that can analyze a patient's electronic health records, genomic data, and other relevant information to recommend personalized treatment plans for complex diseases like cancer. This system can suggest optimal drug combinations, dosages, and treatment schedules tailored to each patient's unique characteristics.

Case Study 4: Drug Discovery and Development

Generative AI is being utilized in the drug discovery and development process, accelerating the identification of promising drug candidates, optimizing molecular structures, and predicting potential side effects and interactions.[4]

For instance, Insilico Medicine, a company specializing in AI for drug discovery, has developed a generative AI platform called GENTRL. This platform can analyze vast datasets to identify promising molecules for clinical trials, optimize their structures, and predict their potential efficacy and safety profiles, significantly expediting the drug development process.

These case studies demonstrate how generative AI is transforming various aspects of healthcare, from medical imaging analysis and virtual patient assistance to personalized medicine and drug discovery. However, it's crucial to address challenges such as data privacy, model bias, and regulatory compliance to ensure the responsible and ethical deployment of generative AI in healthcare settings.