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Use of Artificial Intelligence in Systematic Literature Review

“AI is the new electricity. It has the potential to transform every industry and to create huge economic value. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years” – Andrew Ng

The development of artificial intelligence (AI) and machine learning (ML) in recent years has brought about a revolution in the scientific community with its ability to mimic the human intellect and behavior. While AI is a computer algorithm that can think and act like humans based on what data is being fed into it, ML enables machines to learn from past data or experiences without being explicitly programmed. We foresee a tremendous potential of AI for pharmaceutical industry, particularly in health economics and outcomes research (HEOR) to enable faster decision-making.

A systematic literature review (SLR) follows a transparent and reproducible process to identify and summarize the evidence. An SLR is performed to demonstrate the current state of research on a particular topic, while identifying the gaps and areas for further research. It comprises several time-consuming steps, such as searching and screening for the relevant literature, data extraction, analysis, and proper dissemination of the findings. A reviewer may have to filter the required papers from hundreds or even thousands of papers, and extracting the key information from them is an arduous and error-prone process. Being repetitive in nature, AI could help in simplifying this process.

The attempts to use AI/ML for SLR dates to almost two decades back, with idea of using ML algorithms for SLR as classification techniques to automatically identify the appropriate papers from MEDLINE.[7] Since then, a significant amount of research has been done on inventing and implementing new techniques.

Presently, several semi-automatic AI tools are available for SLR that have been developed to assist the particular step of the SLR process. DistillerSR, Rayyan, PICO Portal, Nested Knowledge and ASReview are few of them designed to help in the screening of the abstracts. They either arrange the articles in the order of most relevant to least relevant based on prediction probabilities or facilitate faster decision-making by highlighting the key words within the abstract. These tools have varying levels of accuracy, recall and workload reduction. Per studies, the use of these tools resulted in a reduction in screening burden of up to 41% and approximately 77% of the screening decisions were accurate.[1-3, 5]

Despite the benefits being reported with the use of these tools, there is still a lack of trust among researchers in SLR automation technologies. Researchers are still in the dilemma of trade-off between benefits and risks of using these tools. On the technical side, some tools require installation and use of Python packages or some computer programming skills that researchers are not familiar with.

Dilemma of trade-off between benefits (i.e., workload and time saving) and risks (i.e., potential to miss relevant records)

Data extraction is the backbone of SLR; it is essential to generate qualitative findings and quantitative estimation. Notably, this is the most time-consuming step and would benefit greatly from automation. However, the main challenge with current AI-based SLR tools is poor performance in data extraction tasks. Even in the AI tools that claim to have fully automated data extraction, data needs to be entered manually into platform by the reviewer. Extracting clinical data implies extracting very specific pieces of information from enormous amounts of text. One of the glitches with automation of data extraction is that different authors may use different terminology and formatting and have different ways of representing the same data, which makes it harder for AI tools trained on a specific type of data to extract data from those papers.[9]

In recent years, the hype is all about large language models (LLMs), including but not limited to ChatGPT. These models are trained on a large dataset of books, documents, web pages etc. and allow them to predict what to say next, as well as summarize the text and generate the text corresponding to a prompt by the user. Certain limitations identified with the use of ChatGPT include using background information for summary, incorrect citations, or not linked to MeSH terms. However, these can be refined and improved in future with the growing power of coding and computing. A recent study showed that ChatGPT performed very well in screening tasks of an SLR as compared to general physicians (GPs). While ChatGPT completed the entire screening process within an hour, GPs took 7-10 days on average. ChatGPT also achieved 95% sensitivity and a negative predictive value of 99%, while also exhibiting workload savings of 40% to 83%.[10]

Retrieval Augmented Generation (RAG) is a technique that allows LLMs to retrieve contextual information from a data source and pass it to the LLM along with the user’s prompt. Providing LLMs with information similar or relevant to the one which the user has requested would help it generate a much better response and help it to improve in tasks like summarizing documents, along with proper prompt engineering.[11] Another feature of LLMs is that they can be adapted to different domains, so an SLR focused LLM would be better at SLR tasks as compared to a model trained on general data. Notably, it is a very fast-growing field, and many more advancements may occur by the time the reader reads this article. Overall, there is a positive outlook on the use of ChatGPT for SLR.

Currently, there is no clear mention of the utilization or endorsement of AI/ML in any available documentation concerning the execution of SLRs for Health Technology Assessment (HTA). While both National Institute for Health and Care Excellence (NICE) and National Center for Pharmacoeconomics (NCPE) anticipate the involvement of two reviewers in the SLR process, they do not explicitly specify whether AI/ML can be considered suitable for one of these roles. Scottish Medicines Consortium (SMC), on the other hand, directs readers to refer to NICE methodologies.

Interestingly, Cochrane, often cited and relied upon by HTA bodies for guidance on best practices in SLRs, is actively engaged in initiatives aimed at comprehending how AI/ML can be effectively harnessed within SLRs to enhance efficiency and the quality of outcomes.[6]

To sum up, the use of AI in SLR has the potential to reduce workload on human researchers and improve efficiency, leading to fast and consistent results. Nonetheless, there is ample work that needs to be done before it can replace human researchers entirely, particularly in the data extraction step of the SLR. Given the increasing popularity of AI tools in SLR, we anticipate their application will increase tremendously by the HEOR community if HTA bodies define the clear guidelines on their use in HTA process.

Authors – Shubhodeep Mitra, Raju Gautam

References:

  1. Hamel, C., Kelly, S.E., Thavorn, K. et al. An evaluation of DistillerSR’s machine learning-based prioritization tool for title/abstract screening – impact on reviewer-relevant outcomes. BMC Med Res Methodol 20, 256 (2020). https://doi.org/10.1186/s12874-020-01129-1
  2. Cichewicz A, Burnett H, Huelin R, Kadambi A: SA3 Utility of artificial intelligence in systematic literature reviews for health technology assessment submissions. Value in Health, Volume 25, Issue 7, Supplement, S604, July 2022
    https://doi.org/10.1016/j.jval.2022.04.1669
  3. M. J. Oude Wolcherink, X. G. L. V. Pouwels, S. H. B. van Dijk, C. J. M. Doggen & H. Koffijberg (2023) Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles?, Expert Review of Pharmacoeconomics & Outcomes Research, DOI: 10.1080/14737167.2023.2234639
  4. de la Torre-López, J., Ramírez, A. & Romero, J.R. Artificial intelligence to automate the systematic review of scientific literature. Computing 105, 2171–2194 (2023). https://doi.org/10.1007/s00607-023-01181-x
  5. van de Schoot, R., de Bruin, J., Schram, R. et al. An open-source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7
  6. Ferizovic N, Rtveladze K. Recommendations on the Use of Artificial Intelligence and Machine Learning in Systematic Literature Reviews Submitted as Part of the Evidence Package in Health Technology Assessment. Value in Health, Volume 25, Issue 12S (December 2022)
  7. Yindalon Aphinyanaphongs, Ioannis Tsamardinos, Alexander Statnikov, Douglas Hardin, Constantin F. Aliferis, Text Categorization Models for High-Quality Article Retrieval in Internal Medicine, Journal of the American Medical Informatics Association, Volume 12, Issue 2, March 2005, Pages 207–216, https://doi.org/10.1197/jamia.M1641
  8. O’Mara-Eves, A., Thomas, J., McNaught, J. et al. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev 4, 5 (2015). https://doi.org/10.1186/2046-4053-4-5
  9. Rito Bergemann, Addressing the Challenges of Artificial Intelligence used for Data Extraction in Systematic Literature Reviews, Parexel 2023
  10. Issaiy, M., Ghanaati, H., Kolahi, S. et al. Methodological insights into ChatGPT’s screening performance in systematic reviews. BMC Med Res Methodol 24, 78 (2024). https://doi.org/10.1186/s12874-024-02203-8
  11. https://help.openai.com/en/articles/8868588-retrieval-augmented-generation-rag-and-semantic-search-for-gpts

 

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Judit Banhazi

Specialty
Value and Access

Role
Vice President

Degree
MD Medicine, JD Law

Judit Banhazi

MD Medicine, JD Law

Judit Banhazi, based in Basel, Switzerland, brings over 20 years of experience in HEOR, Market Access, and Health Policy.
She has led HEOR strategies in hematology and initiated EU HTA policy activities. Judit began her career as a physician and has worked at prime global pharma companies. Her academic prowess is excellent with a peculiar combination of an MD in Medicine and a JD in Law, she has been at forefront of health economics by being involved in HTA policy discussions with EFPIA and HTAi.
Known for her collaborative spirit and practical approach, Judit is passionate about learning and delivering quality work. Outside of work, she enjoys spending time with family and friends, travelling, and running.

Nigel Seear

Specialty
Business Strategy

Role
Non-Executive Director

Degree
MSc

Nigel Seear

MSc

Nigel Seear serves as Non-Executive Director at ConnectHEOR, providing strategic guidance to support the company’s growth, positioning, and long-term vision.

With over 25 years of leadership experience across healthcare, consulting, and professional services, Nigel brings deep expertise in scaling organisations, strengthening leadership teams, and translating strategy into execution. He has held senior roles including Managing Director and Chief Operating Officer within high-growth and private equity-backed businesses, where he has consistently driven sustainable growth and operational excellence.

At ConnectHEOR, Nigel works closely with the leadership team to shape business strategy, enhance organisational effectiveness, and support the company’s evolution as a leading healthcare consulting and technology partner. His perspective combines commercial rigour with a strong focus on people and culture, enabling teams to align around clear goals and deliver meaningful impact.

In addition to his role at ConnectHEOR, Nigel is an experienced board advisor and leadership coach, supporting CEOs and executive teams in navigating complexity, accelerating growth, and building resilient, high-performing organisations.

Aris Skandemis

Specialty
HTA Health Economist

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Senior Consultant, Modeling & Analytics

Degree
MSc – Health Economics

Aris Skandemis

MSc. Health Economics

Aris is a health economist with over seven years of experience in developing economic models for both global and local HTA settings. He holds an MSc in Health Economics from the University of York and a BSc in Pharmacy from the National and Kapodistrian University of Athens. Aris has held senior positions in major health economics consultancies for several years and has worked as an HTA reviewer for the Liverpool Reviews and Implementation Group. 

Aris has extensive experience in multiple disease areas including oncology, cardiovascular, infectious, respiratory, renal, musculoskeletal and rare genetic disorders. He has led over 50 health economic projects in a wide range of markets, including US, Canada, UK and Europe. His experience covers various methodologies and settings, including large scale programs (full HTA evidence generation and submission) as well as nuanced initiatives. Aris also specializes in the development of R/R Shiny models and has drove the upskill programs of several organizations, as well as leading several successful R/R Shiny modelling projects that facilitated concrete, flexible and interactive messaging for stakeholders. 

Aris is based in Athens, Greece and enjoys playing basketball, watching movies and lounging with his dog. 

Adam Ball

Specialty
Business Development Manager

 

Adam Ball

Business Manager

I am delighted to be part of the team here at ConnectHEOR. To tell you a bit about me, I have 10 years experience within Talent Acquisition within HEOR, RWE and Market Access. I have built a global network during this time and am excited to utilize this to help us grow as business. 

 

Outside of work I love sports, playing football and squash regularly, as well as going to the gym. I also enjoy watching sports mainly football and tennis. I have a new born daughter too so she is taking up a lot of my time and is a bundle of joy. I also play drums and like to think I have a broad taste in Music.

 

Eleni Tente

Specialty
Medical writing, Evidence planning

Role
Consultant, Medical writer

Degree
PhD – Molecular biology and genetics

Eleni Tente

PhD – Molecular biology and genetics

Eleni Tente is an experienced medical writer with proven ability to translate complex scientific information into clear, concise, and impactful content to diverse audiences. She has a strong background in integrated evidence planning, publications, internal communications and e-learning development, complemented by an understanding of various therapeutic areas.

Eleni holds a PhD in molecular biology and genetics from the University of Cambridge and an MSc in plant genetic manipulation from the University of Nottingham.

In her free time, Eleni enjoys diving into a good book, fishing along the coast, or planning her next thrilling scuba diving adventure to swim with sharks.

Syed Salleh

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HTA Modelling and Discrete-event Simulation

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Consultant, Modeling & Analytics

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PhD – Health & Related Research

Syed Salleh

PhD. Health & Related Research

Syed Salleh brings extensive experience in HTA modeling, having successfully led the development of both de novo and adaptation models for HTA listings across multiple countries, including Malaysia, Philippines, and the UK. His work spans key therapeutic franchises such as oncology, cardiometabolic, and respiratory. Syed has also delivered critical insights to healthcare professionals through MYSPOR, ITTP, and IKN virtual CME events and numerous publications.

He holds a PhD in Health and Related Research from the School of Health and Related Research (ScHARR) at the University of Sheffield, UK, with a specialization in HTA and operational research, specifically in discrete-event simulation (DES) technique.

During his time in a leading pharmaceutical company, Syed played a key role in securing the listing of several key products in the Malaysia Ministry of Health Formulary and served as the primary contact for DES-related projects.

Besides work, Syed enjoys traveling, listening to music, and spending quality time with his family.

Thai-Son Tong

Specialty
Model Conceptualization and Data Analytics

Role
Senior Consultant

Degree
PhD – Health Economics

Thai-Son Tong

PhD. Health Economics

Thaison Tong has extensive work experience in health economics, decision modelling and big data analysis. He has a unique mix of experience in HEOR and RWE related research in academia and pharmaceutical industry. His expertise lies in health technology assessments (HTA), health economic modelling, simulation modelling, big data analytics and decision analysis. He has hands-on experience in a range of software and programming languages including R, R Shiny, R Markdown, Python, MS Excel, VBA, and Simul8. He has substantial experience of the health care system in the UK and other European countries.

Thaison has direct experience in building cost-effectiveness models from scratch and conducting big data analysis in several disease areas including dementia, vascular disease, and cancer.

Thaison’s PhD focus was to develop a de novo patient level model for the evaluation of different cognitive screening tests for early detection of dementia and mild cognitive impairment in primary care. He also looked at different methods for conducting economic evaluation in health care taking a broader/societal perspective. In addition, he investigated the use of Multiple Criteria Decision Analysis (MCDA) for economic evaluation.

Thaison also holds Academic Researcher position in School of Health and Related Research (ScHARR), University of Sheffield, UK and Honorary Researcher position in University of Bristol, UK.

Thaison’s likes to meditate, and play badminton, basketball and tennis.

Shilpi Swami

Specialty
Consulting and strategy

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Vice President

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MSc. International Economics

Shilpi Swami

MSc. International Economics

Shilpi Swami is an internationally recognized leader in consulting, named among the World’s Top 25 Women Leaders in Consulting 2025 – a distinction that spans the entire consulting industry. She also serves as Chair-Elect of the ISPOR Oncology Special Interest Group and is a frequent speaker at leading scientific and policy forums, where she brings clarity and authority to complex healthcare access challenges.

As Vice President of Consulting and Strategy at ConnectHEOR, Shilpi provides both scientific and strategic leadership, guiding evidence generation and market access initiatives across Europe, North America, Latin America, Asia, and Australia. She has led numerous high-stakes HTA submissions and access strategies, combining methodological rigor with practical solutions that resonate with regulators, payers, and biopharma stakeholders.

Her multidimensional career – spanning academia, consulting, and biopharma equips her with a rare ability to bridge perspectives across research, policy, and industry. Previously a Research Fellow at the University of York, she contributed to advancing best practices in public health and health economics.

Shilpi is widely respected not only for her expertise but also for her people-first approach. She embodies the belief that Kindness is Cost-Effective™, demonstrating that strength in leadership comes from pairing scientific excellence with empathy, collaboration, and impact.

Hugo Pedder

Specialty Statistical Analysis and Evidence Synthesis

Role Senior Consultant

Degree PhD – Statistical Modelling

Hugo Pedder

PhD – Statistical Modelling

Hugo brings in a wealth of experience to ConnectHEOR from his extensive work in academia, focusing primarily on evidence synthesis and meta-analysis. Hugo holds PhD in Statistical Modelling from University of Briston and MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine, and his background in neuroscience remains a passionate interest. Alongside working with ConnectHEOR, Hugo continues to part of NICE committee. His expertise includes advanced indirect treatment comparisons technique and has extensive experience of working with the NICE in UK. 

Beyond professional endeavors, Hugo is an enthusiastic outdoor adventurer, particularly enjoying mountain activities, climbing and ski mountaineering. From building rafts to exploring rivers in north of Sweden, he has lived an adventurous life outside of work and plans to continue to do so.

Kunal Hriday

Specialty
AI Strategy

Role
Director

Degree
MSc. Quantitative Economics

Kunal Hriday

MSc. Quantitative Economics

Kunal Hriday is a business strategy and data science professional with experience in helping organizations crack through notorious business challenges. Kunal is proficient building scalable AI products, specialized Intelligence, automations, business analytics, product lifecycle management and business development. Working as a Data science professional he has spent time in problem solving across variety of industries including Banking, logistics and Health and is now fully dedicated to HEOR. Kunal has hands on experience in various statistical programming tools and languages.

He also holds a masters’ in Quantitative Economics from Indian Statistical Institute and a bachelors’ degree in Business Economics. Excellent in business communication, he is passionate about studying environmental economics and related theories of welfare optimization.

Kunal is trained in Indian Classical Music and finds solace in music when not at work.

Raju Gautam

Specialty
Evidence Review

Role
Principal Consultant

Degree
PhD (Pharmacy)

Raju Gautam

PhD Pharmacy

Raju Gautam spearheads evidence review at ConnectHEOR and  has extensive work experience in evidence review and synthesis, value communications, scientific publications, medical writing and project management.
His expertise lies in systematic and targeted literature reviews, meta-analyses, network meta-analyses, value communications (AMCP and Global Value Dossiers), RWE study design and publications (manuscripts, posters, and abstracts).
He has experience working in Global pharma companies, consulting and CRO environment for several therapy areas including Cardiovascular, Oncology, Neurology, Respiratory, Ophthalmic, Rare Diseases, and Vaccines. He has more than 40 publications in international journals as an author.
Raju also likes jogging, yoga and meditation.

Radha Sharma

Specialty:
Patient preference research, survey, In-depth interviews, COA, Evidence review and conceptualisation of study

Role:
Director – Patient-Centered Outcomes Research

Degree:
MBBS (Bachelor of Medicine and Bachelor of Surgery), PhD (Global Public Health) – University of York

Radha Sharma

PhD (Global Public Health)

Radha Sharma spearheads Patient-Centered Outcomes Research at ConnectHEOR. She has a background in medicine, public health, and epidemiology.

Her expertise includes global health research, preference elicitation, mixed-method studies, consensus workshops, qualitative health research, epidemiological analysis of big data sets, RWE study design, scientific writing, and literature reviews. Her primary focus is integrating patient perspectives into all stages of health technology assessment (HTA) and healthcare decision-making processes.

Her extensive expertise in mixed-method studies and active patient/stakeholder engagement ensures that her research is methodologically rigorous and patient-centric. Radha is an avid hiker and enjoys exploring the beautiful Canadian Rockies.

Kate Ren

Specialty
Statistical Analysis and Evidence Synthesis

Role
Director of Statistics

Degree
Ph.D Probability and Statistics

Kate Ren

PhD Probability and Statistics

Kate spearheads Statistics and Evidence Synthesis at ConnectHEOR. She has more than 10 years of experience in conducting statistical analysis in HTA. Kate has PhD in Probability and Statistics specialising in Bayesian methods in clinical trial design.

She specializes in Bayesian methods in health economics and the elicitation of experts’ beliefs and has extensive experience of conducting evidence synthesis, including, meta-analysis, network meta-analysis, MAIC, STC, ML-NMR etc. Besides working with ConnectHEOR, she is also a part of NICE Committee and University of Sheffield.

Tushar Srivastava

Specialty
Decision Modelling and AI Initiatives

Role
Director

Degree
MSc – Statistics and Computing

Tushar Srivastava

MSc – Statistics and Computing

Tushar Srivastava is a health economics and outcomes research (HEOR) leader whose journey spans academia, consulting, and the life sciences industry – bringing a rare breadth of perspective to evidence generation. Endorsed as a Global Talent by the Royal Society, UK, and holding an MSc in Statistics and Computing, he has authored numerous publications in modeling, indirect treatment comparisons, patient-centered outcomes research, evidence reviews, and AI.

He has built a reputation for uniting scientific excellence with technological innovation. From developing decision models himself to shaping and mentoring world-class modeling teams, Tushar has consistently advanced the science and practice of HEOR with a forward-looking, technology-driven vision.

At ConnectHEOR, Tushar directs HEOR activities and leads the integration of responsible AI into evidence generation. His vision is transforming the field from a human-only framework to an intelligent, interactive, and scalable hybrid intelligence framework – where human expertise works in synergy with AI.

Working diligently to improve patient lives through research and technology, he brings scientific rigor and technological foresight to projects across diverse therapy areas and health technologies, including pharmaceuticals, medical devices, diagnostics, digital health, and public health programs. His work is widely published and presented internationally, reflecting both methodological depth and innovative thinking.

He believes that Kindness is Cost-Effective™ and continues to ensure that ConnectHEOR’s science and technology are always guided by a human-centered ethos.