For every clinical trial, pharmaceutical companies must write a detailed clinical trial report and then distill the hundreds of report pages down to a concise summary for regulators in different countries. However, AI auto-summarization tools do most of this tedious work for less money in less time. Overwise, the process of summarizing clinical trial reports can cost hundreds of thousands of dollars and take several months.
Working with various pharma clients, we at Unicsoft discovered the inordinate resources spent on summarizing clinical trial reports. That was one of the reasons we were driven to develop an AI text summarization tool.
We examined the market and found that existing text summarization tools fall short when dealing with extensive clinical reports. They often omit crucial information, make factual mistakes, and can’t simplify intricate medical jargon for regulators.
Using our experience, we’ll tell you how more reliable AI summarization tools built specifically for clinical trial summarization address these problems. We built such a tool and expect it to reduce costs for pharmaceutical companies by 30% or more. We’ll also discuss how to tackle the current technical limitations of AI tools.
Why do companies write text summaries of clinical trials?
The amount of raw data, complex medical terms, and detailed descriptions of methods of results interpretation in clinical reports are overwhelming for regulators, legislators, and other stakeholders. In addition, reports from international multisite trials must comply with reporting regulations in different countries.
When pharmaceutical, biotechnology, and medtech companies conduct clinical trials to test new drugs and medical treatments, they produce detailed clinical reports about the trial. The reports can span anywhere from hundreds to thousands of pages.
That’s why organizations create summaries — condensed versions of clinical trial reports that communicate critical findings in a digestible and compliant format.
How companies create summaries of clinical trials
Creating a summary is a tedious process that requires several collaborators. Initially, the team identifies what types of information they need to include. They then create a unique template tailored to capture the summary effectively.
Some summary sections might be copied and pasted from the report, while others may need rephrasing for clarity and conciseness. A separate pharma professional performs quality control for charts, tables, and other numerical data. The document usually goes through multiple revisions.
The challenges of text summarization for clinical trial reports
Condensing voluminous clinical trial reports into succinct summaries isn’t easy. Here are the main reasons why:
- Key information is difficult to find. Relevant data is dispersed throughout the lengthy report, making it time-consuming to locate. The process is especially challenging for the medical writers or clinicians who didn’t conduct the study.
- The data is scattered throughout the report. Medical data is often stored in unstructured digital text, making mapping into standard database fields challenging. This information must be extracted manually and reviewed by pharma professionals.
- Overly complex terminology. The specialized terminology in clinical report documents has to be explained and simplified for lay employees, like regulators and legislators.
- Transferring data is tedious. The information transferred from PDFs, case report forms, and TLFs (tables, listings, and figures) always require double-checking by medical writers.
- Auto-summary tools fall short. Regular summarization services and tools are unfit for complex medical documentation. They can make factual errors, errors in data capture, and omit crucial information.
- The summary process is exceptionally expensive. Creating a single summary report can cost hundreds of thousands of dollars. For companies that produce five to ten reports annually, this can amount to millions.
While summaries take a lot of effort, they mainly involve technical tasks. And there’s a way to streamline the most tedious technical work with AI auto-summarization technologies.
Using AI-generated summaries for clinical trial reports
AI auto-summarization technology can help turn long clinical trial reports into informative summaries that contain only the most essential data. An accurate AI summary tool uses various technologies, including natural language processing (NLP), natural language generation (NLG), large language models (LLMs), machine learning, and semantic analysis.
NLP helps the tool understand the text and pick out the key sections. NLG and LLMs can transform information into a coherent text. Semantic analysis dives deep into the text to understand its content within a given context. Additionally, machine learning algorithms continually improve the accuracy of text auto-summarization.
Let’s explore features of AI tools that help produce clear and focused summaries of documents as complex as clinical reports.
A template management feature outlines the structure of the summary (including the format and placement of sections and data tables) and which information to prioritize. A specialized clinical trial AI summarization tool allows you to customize templates to follow international scientific and technical guidelines, like the International Council for Harmonisation (ICH) for the US.
NLP technology can reduce confusion in complex and sometimes overlapping medical terms. It maps different definitions to knowledge graphs, like the International Classification of Diseases (ICD). NLP helps the AI summary tool understand when a single condition is named differently in the report so that the summary can use one term consistently (e.g., “high blood pressure” and “HTN” will simply become “hypertension”).
Automated data extraction
AI summarization tools with NLP and semantic analysis can extract key findings, statistics, and conclusions from clinical trial documents. Optical character recognition (OCR) technology can convert data and words embedded in images in the report to text. In addition, deep learning can fill tables in your template with the relevant extracted information.
Contextual language understanding
Contextual understanding allows the AI summary tool to correctly interpret the meaning of clinical trials, like their goals, methods, and their implications. Meanwhile, NLG and LLM can use alternative phrasing to represent the key insights in plain language, making the findings understandable to people without medical degrees.
Intelligent search with prompt engineering lets you find specific critical information in the enormity of clinical trial documentation. The integration of LLMs lets users ask the AI tool to rank and filter search results based on various criteria and present findings in an easily digestible format (like bullet points).
This feature lets you produce medical summaries in various languages. It also lets companies summarize clinical trial reports and supporting documents from different regions, reducing the time and cost of translation.
An intuitive interface with dashboards helps medical writers and reviewers manage the AI-generated summaries. Among other things, it should allow users to upload documents, track the progress of summary generation, and download reports from a centralized place.
Human review is essential to ensure the accuracy, validity, and completeness of the AI-powered auto-summarization. The AI tool requires a user-friendly collaboration module to help different reviewers approve specific parts of the document, starting from the empty template.
Combining these technologies and features can automate most of the processes involved in creating summaries of clinical trial reports. Now, let’s look at the specific benefits of AI tools for healthcare, pharmaceutical, and medtech companies.
Benefits of AI summary services for clinical trials
AI summary technology reduces the complexity of clinical trial reports that require significant time and expertise to analyze. Here are some of the advantages of using an AI tool for your clinical trial report summaries:
- Reduced cost. AI summary tools can save up to 30% of resources spent on reviewing, analyzing, and summarizing clinical trial documents. With a few cleverly constructed prompts and templates, they can automatically extract all the key information more efficiently than humans.
- Simplification. The AI system makes complex medical text understandable for people without extensive pharma or healthcare knowledge. It automatically translates complicated terms into plain language when needed.
- Customization options. Integrating technologies like LLM into your existing software can improve your employees’ productivity in many data management activities.
- Data security. AI summary tools designed for clinical trials comply with Health Insurance Portability and Accountability Act (HIPAA) regulations. They use role-based access control, authorization mechanisms, and encryption to safeguard patient data and generated summaries.
- Productivity gains. An AI clinical report summary tool can condense clinical trial reports and other medical documents into concise summaries much more efficiently than humans can. It can also translate existing reports and summaries in multiple languages with minimal effort.
AI-powered summary services offer a range of benefits for anyone conducting clinical trials that are hard to ignore. However, we should also mention some challenges with AI summarization.
Challenges of AI text summarization and how to tackle them
Clinical trials are high-risk endeavors, and reporting them requires high precision. While AI tools are generally reliable, there are minimal chances of producing factual mistakes. These errors must be controlled with manual quality assurance reviews.
AI summarization tools can sometimes miss key details. These omissions can range from not specifying what medical intervention was studied to failing to mention the outcomes measured. The problem worsens as the length of the clinical trial and the number of complex tables increases.
However, iterative improvement techniques can help overcome these challenges. Solutions we propose include:
- Focus on the PICO framework. We recommend refining AI models and engineering prompts to focus on the PICO elements — population, intervention, comparison, and outcome. This framework helps the model zero in on the most critical information and avoid unnecessary details.
- Human-assisted reviews. Companies should establish quality assurance metrics to measure the accuracy and completeness of AI-generated summaries. Medical experts should regularly review results and fine-tune the model.
- Train models with machine learning. Machine learning algorithms help AI tools learn and improve over time. You should train your model on successful clinical trial summaries annotated by medical experts.
- Use a post-processing model. It’s possible to implement a secondary model that checks for the completeness of the summary text.
Regular refinement will improve the AI model’s recognition of crucial details from clinical trial reports. The ideal summarization tool can be fully automated and error-free.
Streamlining clinical trial summarization with AI
AI tools with LLM are already making the summarization of medical documents easier. The immediate benefits are clear: AI solutions for healthcare help produce summaries faster and with fewer resources.
As you train and refine your system, it becomes more accurate. The ultimate goal is a fully automated AI-generated summaries of clinical trials that require little to no human editing.
A small investment in AI tools can improve how your organization handles data in complex medical documents. Unicsoft has vast experience delivering AI-powered tools for healthcare and pharma companies. Contact us so we can find a solution for your unique requirements.