AI-Powered Advancements in Systematic Reviews and Meta-Analyses: A New Era of Efficiency and Accuracy
The increased accessibility of generative Artificial Intelligence (AI) has been transforming several industries. However, medical communications professionals have been relatively hesitant to adopt generative AI. Some common concerns are data privacy, the complexity of healthcare data, and regulatory compliance. However, AI could revolutionize our approach to systematic reviews and meta-analysis, an area of relatively low risk. By leveraging AI’s capabilities, medical communications professionals can significantly improve the efficiency and accuracy of their review processes.
Systematic Reviews and Meta-Analysis in Medical Communications
Systematic reviews and meta-analyses serve as the cornerstone of evidence-based medical research. A comprehensive analysis of all available data, as a result of these publications, can guide real-world medical decisions. A systematic review analyzes the existing literature on a specific research question or topic. Such a review requires an extensive search, careful literature selection, and critical evaluation and summarization of relevant studies. In contrast, a meta-analysis contains a statistical analysis of data from multiple studies. A quantitative summary or estimate can be developed based on this analysis to validate the research hypothesis.
Both are crucial in providing rigorous evaluation and validation of a number of medical research questions. They can be used to assess the quality of existing evidence or to increase statistical precision behind medical decisions. They can provide an unbiased approach to establishing a baseline for the current state of research on a specific topic or a therapeutic area. Together, these methods enable researchers, clinicians, and policymakers to make informed decisions, shaping clinical practices based on the most reliable evidence available.
Impact AI on Literature Search and Meta-Analysis for Medical Communications
The volume of medical literature has been growing exponentially. As a result, a manual approach to conducting systematic reviews and meta-analyses is becoming increasingly laborious and time-consuming.1 With AI-powered applications, there is unexplored potential to enhance medical communications. Furthermore, AI can streamline literature screening, improve data synthesis methodologies, and uncover valuable insights. This can empower medical communications professionals to make more informed and impactful contributions at a faster pace.2
AI can empower comprehension, analysis, and processing of human language and data. Machine learning (ML) algorithms, a subset of AI technology, enable systems to recognize patterns and make data-driven predictions without explicit programming. Meanwhile, Natural Language Processing (NLP) focuses on the interaction between computers and human language. Machines can be trained to understand, interpret, and generate human language, facilitating communication and analysis of text data.
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Leveraging these technologies can accelerate medical literature review processes in the following manner:3
1. Efficient Literature Search and Screening
The initial phase of a systematic review involves an exhaustive literature search and subsequent screening to identify relevant studies. Machine learning models can learn from labeled data to distinguish between relevant and irrelevant studies. As a result, the manual workload is minimized, and the risk of overlooking crucial information is reduced. NLP-based methods can augment the identification of closely related candidate keywords to include diverse medical terminology.
2. Data Extraction and Synthesis
AI can automate the extraction of essential data points from selected studies, such as clinical study design, sample size, outcomes, and statistical measures. Particularly, NLP techniques enable the conversion of unstructured text into structured data. This automation not only accelerates the data extraction process but also reduces the likelihood of human error.
3. Statistical Analysis
Meta-analyses involve statistical aggregation of data from multiple studies to derive quantitative conclusions. AI techniques, particularly machine learning, can enhance the precision of meta-analyses by handling complex statistical computations, adjusting for heterogeneity among studies, and identifying sources of variation.
4. Trend and Pattern Recognition
AI can analyze vast datasets to identify trends, patterns, and correlations across studies. Medical communications professionals could harness AI to better identify potential gaps in research, leading to more informed research directions.
5. Automated Summarization and Reporting
AI-powered tools can generate concise summaries of findings, aiding systematic reviewers in presenting results more effectively. These summaries can be formatted for easy inclusion in academic papers and presentations. With NLP integration, more efficient summarization can also be achieved for the purpose of medical marketing or medical affairs.
6. Real-time Updates
AI-driven systems can continuously monitor newly published studies and update systematic reviews and meta-analyses in real-time. This ensures that the analyses are current and reflective of the latest evidence, which can assist medical professionals in staying updated.
In drug discovery and the medical field, the integration of AI into systematic reviews and meta-analyses is particularly promising. AI can streamline the process of evaluating the efficacy and safety of interventions, making the research-to-clinic pipeline more efficient. By harnessing AI’s capacity to analyze large datasets and draw insights from diverse sources, researchers can identify potential targets for drug development, assess treatment effectiveness, and monitor adverse events more comprehensively.
Considerations and Challenges for Integration of AI
While AI offers tremendous potential, it also raises several concerns that must be considered carefully before adopting new technologies. High accuracy and recall, the probability of consistent identification of relevant literature, is paramount.4 Regular audits are necessary to identify and rectify any bias that emerges with AI tools to avoid misidentification of literature. Moreover, the need for transparent and interpretable AI models is crucial to maintaining scientific integrity. In the medical field, this can impact medical interventions and hence, developing AI models that are not only accurate but also interpretable is crucial for building trust in their application. Lastly, as with any medical technology, safeguarding patient privacy and data confidentiality is essential; implementation of effective data management and security measures must be a consideration while building any AI-powered application.
Where are we currently?
Presently, there is a need for rigorous testing and benchmarking that can allow a standardized assessment of the accuracy of AI tools for literature mining.5 The initial results are promising and as AI research continues to advance, the future of systematic reviews and meta-analyses holds exciting possibilities, propelling medical communications into a new era of efficiency and precision. While human oversight will no doubt remain crucial in ensuring the validity and ethical integrity of AI-driven systematic reviews and meta-analyses, AI can assist us by increasing the efficiency of the process.6
Author:
Dr. Gayatri Phadke
Managing Editor, Enago Academy
Connect with Gayatri on LinkedIn
References:
1. Borah, Rohit, Andrew W. Brown, Patrice L. Capers, and Kathryn A. Kaiser. 2017. “Analysis of the Time and Workers Needed to Conduct Systematic Reviews of Medical Interventions Using Data from the PROSPERO Registry.” BMJ Open 7: e012545. https://doi.org/10.1136/bmjopen-2016-012545.
2. van Dijk, Sanne H.B., Marjolein G.J. Brusse-Keizer, Charlotte C. Bucsán, Job van der Palen, Carine J.M. Doggen, and Anke Lenferink. 2023. “Artificial Intelligence in Systematic Reviews: Promising When Appropriately Used.“ BMJ Open 13: e072254. http://dx.doi.org/10.1136/bmjopen-2023-072254.
3. de la Torre-López, José, Aurora Ramírez, and José R. Romero. 2023. “Artificial Intelligence to Automate the Systematic Review of Scientific Literature.“ Computing 105: 2171–94 (2023). https://doi.org/10.1007/s00607-023-01181-x.
4. Bergemann, Rito. 2023. “Addressing the Challenges of Artificial Intelligence Used for Data Extraction in Systematic Literature Reviews.” Parexel International Corporation. https://www.parexel.com/insights/whitepaper/addressing-the-challenges-of-artificial-intelligence-used-for-data-extraction-in-systematic-literature-reviews.
5. Feng, Yunying, Siyu Liang, Yuelun Zhang, Shi Chen, Qing Wang, Tianze Huang, Feng Sun, et al. 2022. “Automated Medical Literature Screening Using Artificial Intelligence: a Systematic Review and Meta-analysis“ Journal of the American Medical Informatics Association 29(8): 1425–32. https://doi.org/10.1093/jamia/ocac066.
6. Blaizot, Aymeric, Sajesh K. Veettil, Pantakarn Saidoung, Carlos F. Moreno-Garcia, Nirmalie Wiratunga, Magaly Aceves-Martins, Nai M. Lai, and Nathorn Chaiyakunapruk. 2022. “Using Artificial Intelligence Methods for Systematic Review in Health Sciences: A Systematic Review“ Research Synthesis Methods 13(3): 353-362. https://doi.org/10.1002/jrsm.1553.