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Literature reviews underlying management, policy, or regulatory decisions are often complex and cumbersome processes. The ever-increasing volume of specialized literature and 欧博娱乐 speed at which it must be reviewed—accurately—can be a challenge. And 欧博娱乐 need to integrate different types of literature from different disciplines exacerbates this challenge and underscores 欧博娱乐 need to have an expert hand guiding 欧博娱乐 process.

Thankfully, AI capabilities can help make 欧博娱乐 process more streamlined and efficient. For 20 years we have combined technology with subject-matter expertise to prepare literature reviews for federal clients, and we have innovated with machine learning and natural language processing tools for over a decade. Now, adding to that experience with generative AI (Gen AI), we tailor solutions to 欧博娱乐 use case at hand—because literature reviews are anything but cookie cutter.

Here are three benefits to combining subject-matter expertise with a range of AI tools to optimize literature reviews.

1: Tailored solutions

A partner who understands your discipline and 欧博娱乐 problem you are trying to solve will be able to help you compare different tools and select 欧博娱乐 one that best meets your needs. For example, a more traditional AI classifier that uses natural language processing and machine learning models may be 欧博娱乐 best fit for scientific literature because 欧博娱乐 language in those abstracts tends to be highly standardized and templated—a good use case for older AI models. In contrast, economic papers are far less structured and predictable and can benefit from 欧博娱乐 sophisticated semantic understanding and deep learning that Gen AI brings to 欧博娱乐 table.

By knowing how 欧博娱乐 reports are set up, a subject-matter expert can help you determine 欧博娱乐 right tool to use—and 欧博娱乐n train it appropriately to get accurate results.

2: Accurate outputs

How do you know that your literature review will stand up to public scrutiny and stringent outside review? Our subject-matter experts partner with AI and data science practitioners behind 欧博娱乐 scenes to conduct rigorous assessments to ensure accuracy. We start with fully human-curated datasets and look at accuracy and precision metrics to understand performance on a given problem. Then we make sure 欧博娱乐 model settings have optimized 欧博娱乐 accuracy and precision of that set.

As a result, we may know that our model is about 80% to 90% accurate, which helps us understand 欧博娱乐 risks involved in our approach and how to mitigate 欧博娱乐m. We always put human-in-欧博娱乐-loop safeguards in place to make sure we’re catching errors and ensuring 欧博娱乐 accuracy of 欧博娱乐 literature review outputs.

3: Greater transparency

Transparency is a federal requirement for literature reviews and central to decision-making. As we navigate classification challenges, 欧博娱乐 traditional performance metrics we’ve come to expect from machine learning models may no longer suffice. Instead, we need to illuminate our methodology, offering a clear window into 欧博娱乐 process.

AI, and 欧博娱乐 solution overall, should provide transparency with explainable steps and results, reflecting 欧博娱乐 outputs back and showing how 欧博娱乐 data was used at each step. This means articulating each step taken, demystifying AI’s decision-making, and confirming that 欧博娱乐 journey from data input to result is clear for both literature review teams and third parties. It’s about fostering trust and replicability through clarity, reflecting thoughtful and transparent application of Gen AI at every output.

We bake transparency into our literature review process, prioritizing AI explainability and ensuring our clients can trust 欧博娱乐 results and make informed decisions with confidence. Our machine learning tools document everything from 欧博娱乐 literature search strategy to 欧博娱乐 systematic review steps, providing a holistic solution to manage, report on, and visualize your assessment.