AI scientific intelligence vs. manual research: comparative performance in 2025
At a time when scientific intelligence is increasingly crucial to staying innovative, R&D and innovation professionals face a major challenge: information overload. For an R&D manager or a scientific watchdog with little time to spare, keeping track of all advances manually becomes almost mission impossible. In other words, any tool that can speed up monitoring without compromising on quality is of great interest.
Fortunately, new artificial intelligencesolutions dedicated to monitoring are emerging. These automated technology watch tools promise to increase efficiency by filtering and synthesising information instead of humans.
In this article, we compare the performance of a traditional manualwatch and an AI-automated watch on the same cross-disciplinary scientific subject.
We have chosen a topical subject: the impact of climate change on health.
Contents :
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1. Traditional method (manual search)
The traditional manual search method involves several laborious stages.
Identification of sources and initial requests
First, the researcher identifies relevant sources: large bibliographic databases (such as PubMed, Scopus, Web of Science). They formulate keyword-based queries (e.g. “climate change health impacts”) and apply filters (dates, languages, disciplines) to obtain an initial set of results.
Manual selection and analysis of articles
Then comes the selection: sifting through dozens or hundreds of titles and summaries to choose the most relevant articles. Each article selected has to be read, analysed and its key information extracted manually.
Summary and final report
Finally, you need to synthesise this scattered data into a coherent overview – usually in the form of notes or a report.
Main impact of climate change:
- Deaths and morbidity: +250,000 annual deaths, +489,000 heat-related deaths (2000-2019); extreme episodes such as 2003 responsible for +70,000 deaths in a few months who.int.
- Cardiovascular health: strong correlation between extreme heat, ozone, storms and cardiovascular disease: increased mortality and hospitalisations.
- Infections & malnutrition: impacts on malaria, diarrhoea, malnutrition and hunger in LMIC; projections of increased severity by 2050 pubmed.ncbi.nlm.nih.gov+1pubmed.ncbi.nlm.nih.gov+1.
- Mental health & young people: post-traumatic stress, psychiatric disorders, hospitalisations, preterms and respiratory illnesses in children .
- Inequalities & vulnerabilities: the majority of deaths in countries with poor infrastructures, elderly populations, minorities, children .
At each stage, adjustments had to be made: new searches with different keywords, following the bibliographic references of a source article, and so on. In the end, we often end up with dozens of PDFs to go through and a mass of scattered notes to organise into a usable summary.
This manual process is very time-consuming. For a vast subject like our example, it is not uncommon to spend long days, even several weeks, between the initial research and the writing of a summary.
2. Automated method with Opscidia (AI platform)
Let’s now move on to an automated method using an AI platform, such as Opscidia. The aim is toautomate a literature review on our chosen topic, in order to quickly extract the essential information.
Search settings: keywords and filters in Opscidia
Users begin by entering their search criteria: (keywords such as “climate change human health”, possibly refined by date or domain filters). Opscidia’s AI search engine then searches its vast corpus (over 200 million scientific articles, theses and patents) in a single query.
Quick sorting and recommendation of publications
In just a few seconds, the platform sorts all the search results into a table: based on the number of citations and relevance, to recommend the most relevant publications – drastically reducing the time spent on initial sorting.
From this consolidated list of results, watchers can select the documents that interest them most. Rather than downloading and reading each PDF in its entirety.
Optimising search: use AI to interrogate PDF content directly
It uses Opscidia ‘s AI data extraction function.This tool makes it possible toquery the content of PDFs directly: for example, you could ask “what are the main effects of climate change on health according to this article?” or “what key results emerge from the study? The AI will instantly analyse the text of the document and provide a targeted response, indicating precisely the source in the article. This eliminates the need to manually scroll through each page in search of the key information – saving considerable time and energy.
Search trends: automatically generated graphs and tables
The platform also offers automatic visualisations to help analyse underlying trends. With just a few clicks, it is possible to generate graphs and tables from all the bibliographic data on the subject. For example, Opscidia can displaytrends in the number of publications on “climate change & health”over time, revealing any peaks of interest over the years.
In-depth analysis of the ecosystem with AI
It is also possible to obtain a ranking of associated emerging terms (to detect trends or ‘weak signals’), identify the companies or organisations filing the most patents in the field, or visualise the geographical distribution of publications (which countries publish the most articles on this theme). These automatically generated infographics offer an overview of the researchecosystem that would be very difficult to reproduce manually by compiling hundreds of references.
Finally, Opscidia ‘s AI moduleenables users to create a finalsummary of the intelligence gathered. Rather than writing a report from scratch, users can ask the platform to generate a summary of the information collected. The AI will then write a condensed text highlighting the key points of the selected publications, while rigorously citing the original references. The professional remains in control, of course: he or she can adjust the proposed summary, check the sources, and add his or her own analysis and expertise. The idea is not to replace the human, but tohelp them write faster. As the saying goes, ” AI suggests, the expert disposes“: the tool suggests content, and the expert is free to accept, modify or add to it as he sees fit. The end result is a high-quality, comprehensive and well-sourced monitoring report, produced in a fraction of the time it would take to conduct a traditional review.
3. Comparison of results
To sum up, here are the main differences between manual monitoring and automated monitoring using AI in our example:
- Time spent:Manual monitoring requires a very significant investment of time (several days’ work for a complex subject), whereas the AI approach enables you to move much faster, it is estimated that it reduces research time by around 50% on average.
- Completeness :A human, even an experienced one, can only read a limited number of articles, which increases the risk of missing relevant information. Conversely, AI can review a much larger corpus (up to millions of documents) and provide a more exhaustive summary of the knowledge available.
- Cost:The traditional method requires skilled (and therefore expensive) human time and may require paying access to multiple databases. The AI platform, on the other hand, represents a subscription or software investment, but one that quickly pays for itself thanks to the time saved and the increased productivity of the R&D team.
- Quality and reliability:Manual work offers a high quality of expert analysis, at the cost of considerable effort, and remains subject to human bias or error. AI, on the other hand, delivers highly accurate and consistent results (for example, by faithfully extracting key data and citing sources). However, it can miss nuances that only the trained human eye will notice. The combination of the two – the power of AI and validation by the expert – ultimately ensures the best reliability.
- Trend analysis: AI tools offer additional functionalities (dashboards, dynamic graphs) for spotting emerging trends or weak signals in the mass of information, which would be very difficult to do manually without a great deal of data science work. What’s more, a platform like Opscidia can continuously monitor new publications and alert the user, ensuring proactive monitoring where manual methods often react with a delay.