In the rapidly evolving landscape of research and analysis, technology is continuously shaping how we approach systematic reviews. Among the latest innovations, Chat GPT (Generative Pre-trained Transformer) has garnered significant attention for its potential to assist researchers in the review process.
While it undoubtedly offers valuable contributions to the field, it is essential to clarify that Chat GPT is not a cure-all and cannot replace systematic review tools like Laser AI. To understand why, let's first explain the context.
Before we explore Chat GPT, it is crucial to understand the foundation of systematic reviews. A systematic review is a comprehensive and unbiased research type involving synthesising evidence on a particular research question. It follows a predefined set of protocols, meticulously searching, appraising, and analysing relevant studies to provide robust conclusions and inform evidence-based decision-making.
Chat GPT, on the other hand, is a natural language processing chatbot that utilises deep learning to generate human-like text responses based on prompts. Developed by OpenAI, Chat GPT is pre-trained on an extensive text collection, allowing it to understand context, grammar, and semantics. Its capacity to engage in conversation-like interactions makes it a promising tool for aiding researchers in various domains.
Chat GPT functions through a process known as "unsupervised learning." During its pre-training phase, it processes vast amounts of biased and unbiased data from books, articles, and websites to develop a language model. This model is then fine-tuned on specific tasks through "supervised learning" to make it more targeted and domain-specific. Consequently, when given a prompt or query, Chat GPT generates responses by predicting the most probable sequence of words based on its training.
It's important to note that while Chat GPT can generate impressive responses, it is not inherently aware or conscious of the meaning of its output. It generates answers based on statistical patterns in the training data and does not fully understand or comprehend the content it produces. Because of this, bias and hallucination can emerge in responses due to the preferences in the training data or fine-tuning process.
Laser AI is an AI-powered systematic review tool that helps researchers accelerate the process of identifying, assessing, and synthesising evidence. It empowers reviewers to work more efficiently and significantly reduces their workload. Laser AI uses various AI techniques, including natural language processing and machine learning to automate many tasks involved in systematic reviews. This can save researchers a significant amount of time and effort and help improve the quality of the reviews.
While Chat GPT and Laser AI are both systems that use AI to complete their tasks, they have different goals and backgrounds. Laser AI is built on a foundation of security and compliance to help organisations complete systematic reviews and is regularly audited by independent security firms like SOC 2, ISO 27001 and FedRAMP. ChatGPT, on the other hand, is not wholly as focused on security and compliance as Laser AI but is still a powerful tool for various purposes. Detailed differences are presented in the table below.
To answer the question, Chat GPT is not the future of systematic reviews. AI designed for this specific task will be the future of completing systematic reviews.
Systematic reviews are relied upon for evidence-based decision-making in various fields, including medical research and policymaking. Ensuring the accuracy and reliability of the findings is paramount, which is why human intervention remains essential in the systematic review process. While Chat GPT can aid in automating specific processes, its use for systematic reviews is sceptical because of the lack of security and inaccurate information.
Using Laser AI, a tool explicitly designed to aid researchers and analysts with systematic reviews that fall within the best security practices, is in the best interest of those needing correct and valuable information.
Evidence Synthesis Specialist at Evidence Prime. She is responsible for testing new solutions in Laser AI and conducting evidence synthesis research.
As a passionate writer with a strong drive for strategic growth, Shelby leverages storytelling techniques to provide value for Evidence Prime's audience.
Explore why alternative approaches to ChatGPT should be used in evidence synthesis. Learn about Laser AI vs ChatGPT in evidence-based healthcare.READ MORE
Related blog posts: