AI-powered Literature Reviews: Efficiency or overreach?

How medical writers can smartly and ethically harness AI to navigate the flood of scientific data

In the age of information overload, conducting a thorough literature review is both more essential yet more overwhelming than ever. With biomedical research growing at an exponential rate, medical writers and researchers face the daunting task of sifting through thousands of papers to extract relevant, high-quality evidence. Enter artificial intelligence (AI): a tool that promises to accelerate the process, reduce manual workload, and highlight key insights in minutes rather than weeks. But how much can, and should we, rely on AI to perform such a critical task?

The promise of AI in literature reviews

AI-powered tools like Semantic Scholar, Elicit, ResearchRabbit, and ChatGPT-based assistants offer increasingly sophisticated support for literature review tasks. These platforms can:

• Scan vast databases of biomedical literature
• Cluster similar findings by theme or methodology
• Track citation networks
• Summarise results or compare findings across studies
• Highlight research findings

For medical writers working on clinical study reports, regulatory submissions, or scientific manuscripts, the efficiency gains are undeniable. AI can identify relevant articles within seconds, reduce time spent on manual screening, and help structure review sections with thematic coherence. In environments where timelines are tight and precision is critical, this support can be a game-changer.

The benefits: Speed, scale, and discovery

Faster turnaround: AI accelerates the initial search and synthesis process, allowing medical writers to move from idea to outline far more quickly. This can be especially helpful in early project scoping or when writing for fast-paced medical affairs teams.

Wider reach: Unlike human reviewers, who may subconsciously gravitate toward familiar journals or authors, AI can cast a broader net. This reduces the risk of citation bias and helps surface relevant studies from less obvious sources.

Improved consistency: AI systems can apply consistent logic to identifying and categorising articles, helping writers maintain structure and tone across multiple review sections or projects.

Insight discovery: AI tools can help highlight trends, themes, and knowledge gaps that may not be immediately obvious in manual searches, aiding strategic content development.

The limitations: Accuracy, context, and critical judgment

Despite its impressive capabilities, AI still has critical limitations and relying on it without oversight can lead to potentially serious errors, including the following.

Citation hallucinations: Some AI tools, particularly general-purpose language models like ChatGPT, may generate citations that appear credible but are entirely fabricated. This can undermine the integrity of a medical document and damage trust. Never rely on citations without a proper look-up (and, when looking them up, be sure to assess the quality of them).

Contextual blind spots: AI tools often lack deep domain understanding. They may misinterpret nuanced concepts, overlook key study limitations, or fail to distinguish between high- and low-quality evidence.

Outdated or incomplete databases: AI tools are only as good as the data they have access to. If they rely on outdated databases or exclude recent publications, critical evidence may be missed.

Inability to assess study quality: AI cannot reliably evaluate methodological rigor, bias, or statistical flaws. These assessments require human expertise and experience in interpreting research design and clinical relevance.

A balanced approach: AI as an assistant, not the authority

So, is AI-powered literature review a revolution or a risk? The answer lies in how it is used.

AI should be treated as a smart assistant, not an autonomous reviewer. Used ethically and strategically, it can:

• Triage large volumes of literature
• Suggest relevant keywords or themes
• Generate a starting point for narrative synthesis
• Reduce fatigue during initial stages of research

But it must be followed by a rigorous, human-led evaluation process. Medical writers should always:

• Cross-check AI-identified studies in trusted databases
• Verify all citations and references
• Apply critical appraisal frameworks to assess quality and relevance
• Ensure content meets ethical, regulatory, and scientific standards

The role of the medical writer in the AI era

Far from making human writers obsolete, AI highlights just how essential skilled professionals remain. The most effective medical writers in today’s landscape are those who:

• Embrace AI to improve workflow and productivity
• Understand the limitations of AI-generated outputs
• Uphold accuracy, clarity, and clinical integrity in all content
• Stay informed about emerging tools and best practices
• Advocate for transparency and responsible AI use in their teams and organisations

In summary

AI-powered literature review tools offer powerful support in managing the deluge of biomedical data. They can enhance speed, widen reach, and unlock insights—but they are not infallible. The best results come when human expertise and AI efficiency are combined.

For medical writers, the goal is not to compete with AI but to work alongside it. When used ethically and wisely, AI becomes a valuable co-pilot on the road to high-quality, evidence-based content.

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Frequently Asked Questions (FAQs) 

What you need to know about AI-powered literature reviews in medical writing

Q: What is an AI-powered literature review?
A: It’s a review process that uses artificial intelligence tools to scan, summarise, and categorise biomedical research. These tools help medical writers quickly identify relevant studies, uncover trends, and organise findings, speeding up early-stage research and content development.

Q: What are the benefits of using AI in literature reviews?
A: AI tools improve efficiency by scanning vast volumes of literature, reducing manual screening, and identifying themes or research gaps. They help identify relevant studies, minimise citation bias, and offer consistent categorisation, saving time while enhancing quality. Importantly, they can also help uncover less obvious but still relevant studies, improving the capture of research outside a typically defined search field.

Q: Can AI tools replace medical writers in literature review tasks?
A: No. While AI can support the process, it cannot assess study quality, interpret complex findings, or ensure clinical relevance. Human writers are essential for evaluating methodology, verifying citations, and maintaining ethical and scientific standards.

Q: What are the risks of relying too heavily on AI for literature reviews?
A: Risks include citation “hallucinations” (AI generating fake or incorrect references), misinterpretation of study context, use of outdated or incomplete data, and an inability to critically assess research quality. Without human oversight, these risks can compromise content accuracy and credibility.

Q: How can medical writers use AI tools ethically and effectively?
A: Treat AI as a supportive assistant, not a decision-maker. Use it to identify sources, explore themes, and streamline early drafts. Always verify citations, cross-check data in trusted databases, and apply human judgment to ensure content quality and integr

Q: Which AI tools are commonly used for literature reviews?
A: Popular tools include Semantic Scholar, Elicit, ResearchRabbit, and ChatGPT-based assistants. Each offers different features, such as citation tracking, thematic clustering, and summarisation, to help streamline research workflows.

Q: Can AI improve the quality of literature reviews?
A: Yes, when paired with expert oversight. AI can broaden the scope of literature searches and reveal patterns or gaps, which can lead to more comprehensive and insightful reviews. However, human writers must still critically appraise the findings and draw reliable conclusions.

Q: Is it safe to use ChatGPT for medical literature reviews?
A: ChatGPT can help brainstorm themes or summarise known content, but it may generate inaccurate citations or outdated information. It should never be used to replace verified databases or human-led evaluations in medical writing.

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Looking for medical writing support that blends innovation with integrity? 

At KMG Communications, we use AI to support - but never replace - the expert thinking and critical judgment of our team. Every literature review we produce is shaped by experienced medical writers who understand nuance, address research expectations, and appreciate the need for precision in health communication.

Contact KMG Communications. While we integrate aspects of AI into our work practices to inform the development of medical content, our work is always backed by real, human expertise.

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References

Bolaños F, Salatino A, Osborne F et al. 2024. Artificial intelligence for literature reviews: opportunities and challenges. Artificial Intelligence Review, 57: 259. https://doi.org/10.1007/s10462-024-10902-3

Guler N, Kirshner SN, Vidgen R. 2024. A literature review of artificial intelligence research in business and management using machine learning and ChatGPT. Data and Information Management, 8(3): 100076. https://doi.org/10.1016/j.dim.2024.100076

Ilegbusi PH. 2024. The integration of Artificial intelligence (AI) in literature review and its potentials to revolutionize scientific knowledge acquisition. AfricArXiv. https://doi.org/10.21428/3b2160cd.50b471d6

Mostafapour M, Fortier JH, Pacheco K,Murray H, Garber G. 2024. Evaluating literature reviews conducted by humans versus ChatGPT: Comparative study. Journal of Medical Internet Research AI, 3:e56537 doi: 10.2196/56537. https://ai.jmir.org/2024/1/e56537

van Dijk SHB, Brusse-Keizer MGJ, Bucsán CC, van der Palen J, Doggen CJM, Lenferink A. 2023. Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open,13(7):e072254. doi: 10.1136/bmjopen-2023-072254. https://pmc.ncbi.nlm.nih.gov/articles/PMC10335470/

Wagner G, Lukyanenko R, Paré G. 2022. Journal of Information Technology, 37(2): 209-226. https://journals.sagepub.com/doi/pdf/10.1177/02683962211048201

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Author: Kara Gilbert, KMG Communications