DistillerSR AI

Scientifically Validated AI

DistillerSR is a pioneer in the use of AI for literature reviews.
Since 2016, customers have trusted DistillerSR AI to accelerate
the completion and enhance the quality of their reviews.

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AI That Learns From Your Data

DistillerSR AI autonomously trains itself on your data as you work. After only a few references, it quickly learns to recognize what references/studies/articles are relevant and then begins assisting with the screening process, reducing time to literature review completion by as much as 70%.

AI Reprioritization Simulation, DistillerSR

The diagonal line represents traditional screening methods while the green line represents the time saved with continuous AI reprioritization.

Faster Evidence Generation

Reduce title and abstract screening by up to 70%, with greater automation and precision.


Re-rank learns from your reviewers and reprioritizes references based on how you include and exclude them.

Quality Control

Validate reference inclusion and exclusion decisions made by humans or AI. Find potential errors in seconds.

More Efficient

Automatically answer closed-ended questions, find similarities between texts, and make predictions up to 4x faster.

Accessible Domain Specific AI

DistillerSR AI lets you easily build and deploy custom AI Classifiers using data you are already collecting. AI Classifiers can then be used to automatically label references, identify key elements in a paper, or serve as a second reviewer. They produce predictable and consistent outcomes, explainability and, if required, streamlined human-in-the loop validation.


Start evaluating and extracting data from citations sooner by automatically pushing references to later stages of the review.


Automate workload assignment management to fully leverage your subject matter experts and assign references to particular forms or reviewers sooner.

AI Quality

Increase the thoroughness of your reviews by having classifiers operate in tandem with your reviewers to double-check how you’ve categorized citations.

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Geistlich Reduces Literature Screening by 85%

“By learning my inclusion and exclusion pattern and reordering references based on relevance, DistillerSR AI enabled a more efficient overall review process and faster literature review completion rates.” Shelley Jambresic, Senior Clinical Evaluation Manager at Geistlich Pharma AG

An alligator on the water's surface

Health Agency Uses AI to Reduce COVID-19 Citation Screening and Data Extraction By 50%

During the pandemic, DistillerSR AI Classifiers made reviewers more productive by saving them time and streamlining their workloads by automatically triaging citations between teams, as the volume of work grew rapidly.

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University of Guelph Cuts Screening Time by Almost 50% with DistillerSR’s AI

“We used DistillerSR AI in a recent review and it helped cut the workload by about half. We were able to confidently stop screening with only half of the references screened.”

– Jan M. Sargeant, Professor, Department of Population Medicine, Ontario Veterinary College, University of Guelph

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CRO Saves up to 30% Using
DistillerSR AI

“We are currently saving up 20 to 30% of time and resources. Instead of having 2 reviewers per project, we now have 1 reviewer. We can focus on validating DistillerSR AI Classifiers instead of simply focusing on extracting data. We can now run larger projects and generate more revenue because we can take on more projects.”

– Remon van den Broek, Head of AI Strategy and Data Science, Adelphi Group

Eli Lilly’s Assessment of DistillerSR AI

At the professional society for health economics and outcomes research (ISPOR) Europe 2023 Conference, Eli Lilly Services India concluded that applying DistillerSR AI in title-abstract screening reduced 67% of human efforts (hours) irrespective of the type of literature review. It also reported that DistillerSR AI had a mean accuracy score of 90% for traditional systematic and targeted literature reviews.

Read the conference poster

DistillerSR AI and LLMs: What the Future Holds

Large language models (LLMs) are poised to play a critical role in literature reviews. Recognizing this, DistillerSR is heading an international consortium of companies, government agencies and academic institutions with the goal of fully automating the extraction of text and data from scientific literature. This undertaking is combining the scaling power of deterministic AI, large language models (LLM), and other technologies to produce reliable, explainable and fully automated results. This project is being funded by a number of national governments as well as the private sector, DistillerSR, Philips, University of Leiden, Synerscope, Ossur, Adelphi Group, Biotronik, and the Fraunhofer Institute for Experimental Software Engineering have joined forces in this initiative.

With industry, regulatory and academic stakeholders working together, consortium members believe these technologies, trained, and validated to therapeutic and healthcare domains, will allow academic and commercial research to identify risk of bias, establish contextual longitudinal assessments of data residing in non-scientific or technical literature, and identify and characterize commonalities and causal relationships across data sources – delivering clinical evidence faster to physicians, regulatory bodies and health authorities.

Scientifically Validated AI For Literatures Reviews

Implementing AI Vertical use cases – Scenario 1
Stefano Cagnonia,​ Vieri Emilianib,​ Gianfranco Lombardoa,​ Wynand Alkemac,​ Carlijn Hooijmansd 2023

Error rates of human reviewers during abstract screening in systematic reviews
Zhen WangID,​ Tarek Nayfeh,​ Jennifer Tetzlaff,​ Peter O’Blenis,​ Mohammad Hassan Murad 2020

Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses
C Hamel,​ M Hersi,​ SE Kelly,​ AC Tricco,​ S Straus,​ G Wells,​ B Pham,​ B Hutton 2021

An evaluation of DistillerSR’s machine learning-based prioritization tool for title/abstract screening – impact on reviewer-relevant outcomes
C Hamel,​ SE Kelly,​ K Thavorn,​ DB Rice,​ GA Wells,​ B Hutton 2020

Evaluating the efficacy of artificial intelligence tools for the automation of systematic reviews in cancer research: A systematic review
X Yao,​ MV Kumar,​ E Su,​ A Flores Miranda,​ A Saha,​ J Sussman 2023

Application of Artificial Intelligence in Literature Reviews 
Shiva Kumar Venkata,​ Sravani Velicheti,​ Vinayak Jamdade,​ Sreeja R,​ Monika Achra,​ Kushal Kumar Banerjee,​ Chandreyee Dutta Gupta,​ Michael Happich,​ Annabel Barrett Eli Lilly Services India Private Limited India,​ Eli Lilly and Company​ United Kingdom 2023

Using an artificial intelligence tool can be as accurate as human assessors in level one screening for a systematic review
JK Burns,​ C Etherington,​ O Cheng-Boivin,​ S Boet 2021

Related Resources

The Case for AI Icon, DistillerSR

The Case for AI in Systematic Reviews

Learn how using AI can dramatically reduce your screening burden and produce faster, more accurate literature reviews.

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Leveraging AI-Enabled Classifiers for Faster, More Efficient SLRs

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Health Agency

Learn how this Health Agency used AI to reduce COVID-19 citation screening and data extraction by 50%.