Drug development depends on evidence.
Before a clinical programme advances, teams need to understand what has already been studied, which mechanisms have been investigated, what outcomes have been reported, where evidence is consistent, and where important gaps remain.
Literature reviews provide much of that foundation.
Yet the process of finding, screening, extracting, organising, and synthesising scientific literature remains heavily manual across many pharmaceutical organisations. Researchers may spend hours constructing searches, screening titles and abstracts, reviewing full texts, extracting study characteristics, checking references, and updating evidence tables.
The visible cost is researcher time.
The hidden cost is broader.
Manual literature reviews can delay decisions, create inconsistent workflows, make evidence updates difficult, increase the risk of missed publications, and leave highly skilled scientists spending too much time on repetitive information-processing tasks.
This is where an AI research platform can change the economics of evidence discovery.
The objective is not to remove researchers from the review process. It is to automate repetitive activities, accelerate evidence discovery, and give scientific teams more time to evaluate what the evidence actually means.
Scientific literature can influence decisions throughout the drug development lifecycle.
Teams may review published evidence when:
Selecting or validating a therapeutic target
Understanding disease biology
Assessing a mechanism of action
Designing a clinical trial
Evaluating potential endpoints
Identifying competing approaches
Preparing regulatory strategies
Monitoring emerging safety information
Supporting medical affairs
Developing evidence-generation strategies
Assessing external innovation
Informing portfolio decisions
When organisations calculate the cost of literature reviews, they often focus on labour hours.
That is only the beginning.
Consider the workflow:
Search → Deduplicate → Screen → Retrieve → Review → Extract → Validate → Synthesise → Report → Update
Every stage consumes time.
A senior researcher may spend only a few minutes screening an individual abstract. But thousands of abstracts can quickly become hundreds of hours of work.
The opportunity cost can be even greater.
A scientist spending the day screening publications is not spending that time:
Interpreting emerging evidence
Designing experiments
Challenging scientific assumptions
Supporting development strategy
Investigating unexpected findings
Collaborating with clinical teams
Communicating scientific implications
The first cost appears before screening even begins.
Researchers must decide which databases to search, construct search strings, select keywords, identify synonyms, and refine queries.
A single scientific concept may have several names.
A target can be referred to by an official name, historical name, gene symbol, protein name, pathway designation, or commercial terminology.
A disease may also have multiple classifications and terminology variations.
Researchers therefore need to build comprehensive search strategies.
This work requires expertise, but much of the operational process is repetitive.
AI can assist by expanding concepts, identifying related terminology, and helping researchers develop broader search strategies.
The researcher remains responsible for determining whether the search strategy is scientifically appropriate.
The difference is that AI can reduce the mechanical effort involved in constructing and iterating searches.
Missing a relevant publication can have consequences beyond one incomplete search result.
A missed study may affect:
An evidence assessment
A scientific conclusion
A trial design decision
A competitive assessment
A safety interpretation
A systematic review
A regulatory discussion
After a search is performed, researchers often need to screen titles and abstracts.
This is one of the most repetitive stages of a literature review.
A typical workflow may involve asking:
Does the study address the right population?
Is the intervention relevant?
Is the disease area relevant?
Does the study report an outcome of interest?
Does it meet the predefined study-design criteria?
Should the paper move to full-text review?
Each individual decision may be straightforward.
The problem is scale.
If a search produces several thousand records, even a fast reviewer can spend substantial time screening them.
This is one of the areas where systematic literature review automation can provide significant value.
AI can help rank or classify records according to predefined criteria, allowing researchers to focus first on publications most likely to be relevant.
This does not mean automatically accepting every AI classification.
Human reviewers can validate inclusion and exclusion decisions, particularly for high-impact reviews.
The value comes from reducing the amount of repetitive screening that experts need to perform.
Abstract screening is only one stage.
Potentially relevant studies often need to be retrieved and reviewed in full.
Researchers may need to identify:
Study population
Intervention
Comparator
Study design
Sample size
Endpoints
Outcomes
Follow-up period
Statistical methods
Limitations
Relevant subgroup findings
This creates a more efficient workflow:
AI finds → Researcher verifies → Team interprets
That is fundamentally different from asking an AI system to independently decide what the evidence means.
Once relevant studies have been identified, teams often create evidence tables.
These may contain fields such as:
Study characteristic | Example information |
Study design | Randomised controlled trial |
Population | Defined patient population |
Intervention | Investigational treatment |
Comparator | Standard of care |
Sample size | Number of participants |
Primary endpoint | Defined clinical outcome |
Secondary endpoints | Additional outcomes |
Follow-up | Study duration |
Key findings | Main reported results |
Limitations | Important study limitations |
Building these tables manually can take considerable time.
Researchers need to locate information, copy it into structured formats, check transcription, standardise terminology, and verify that the extracted information accurately represents the source.
AI can assist with structured extraction.
It can identify candidate information and populate evidence fields for human review.
This can significantly reduce repetitive data-entry work.
The important control is traceability.
Each extracted data point should remain connected to the source document and relevant passage where practical.
One of the biggest weaknesses of manual reviews is that evidence does not stop changing when the review is completed.
New research continues to appear.
A review completed in January can have additional relevant publications by April.
This creates a maintenance problem.
Teams may need to repeat searches periodically to determine whether the evidence base has changed.
For fast-moving therapeutic areas, the process can become almost continuous.
An AI-enabled workflow can help monitor new literature and identify publications that may affect an existing evidence set.
This changes the model from:
Review once → Archive
to:
Review → Monitor → Update → Validate
That is particularly useful in areas such as oncology, immunology, infectious disease, rare disease, and emerging therapeutic technologies where scientific evidence can evolve rapidly.
Another hidden cost comes from fragmented information.
A pharmaceutical organisation may have literature stored across:
Shared drives
Reference managers
Spreadsheets
Document repositories
Individual researchers' folders
Internal knowledge platforms
External databases
Scientific publications rarely exist in isolation.
One study may provide evidence about a mechanism.
Another may investigate the same mechanism in a different disease.
A third may evaluate a related compound.
A fourth may report an unexpected safety finding.
Researchers need to connect these pieces.
AI can help identify relationships across literature based on:
Disease
Target
Mechanism
Molecule
Biomarker
Endpoint
Population
Study type
Research organisation
Investigator
The opportunity for AI literature review pharma workflows is therefore broader than simply asking an AI model to summarise papers.
A more useful workflow can include:
Search strategy development
Literature discovery
Record classification
Deduplication support
Relevance ranking
Full-text retrieval
Structured extraction
Evidence comparison
Gap identification
Citation tracking
Continuous monitoring
Human validation
It is important to distinguish these two activities.
"What does this paper say?"
"What does the body of evidence across these papers collectively show?"
The second question is considerably more complex.
It may require evaluating differences in:
Study populations
Trial designs
Sample sizes
Endpoints
Statistical methods
Follow-up periods
Comparators
Sources of bias
A traditional workflow might require researchers to perform each stage sequentially.
AI can accelerate several stages in parallel.
For example:
Traditional
Search → Screen → Retrieve → Read → Extract → Compare → Summarise
AI-assisted
Search + semantic discovery → Prioritised screening → Targeted retrieval → Assisted extraction → Evidence mapping → Expert synthesis
The benefit is not that every step becomes automatic.
The benefit is that the overall cycle can become shorter.
This matters when literature findings need to inform time-sensitive decisions.
A clinical development team designing a protocol may not have weeks to wait for a comprehensive evidence review.
A medical affairs team monitoring an emerging safety issue may need to understand new evidence quickly.
A strategy team assessing an emerging therapeutic approach may need to identify relevant research before a portfolio discussion.
Speed can therefore become a strategic capability.
Manual reviews can vary depending on who performs them.
Two researchers may:
Use different search terms
Interpret inclusion criteria differently
Extract information differently
Organise evidence differently
Prioritise different findings
A modern AI research platform can bring multiple research activities into one environment.
Useful capabilities may include:
Search by concepts and relationships rather than only exact keywords.
Identify documents relevant to the research question even when terminology differs.
Find relevant information within long scientific documents.
Organise study characteristics and findings into predefined fields.
Connect publications with diseases, targets, mechanisms, products, and outcomes.
Maintain links between insights and source material.
Allow researchers to share evidence sets and findings.
Identify newly published research relevant to an existing question.
Control access and maintain appropriate records of research workflows.
These capabilities help shift literature review from a document-by-document activity to an evidence intelligence process.
AI should not be treated as a replacement for scientific expertise.
Researchers remain responsible for determining:
Whether a study is methodologically sound
Whether evidence is relevant
Whether findings are clinically meaningful
Whether studies are sufficiently comparable
Whether limitations change interpretation
Whether conclusions are justified
Whether evidence supports a development decision
A trustworthy literature workflow should allow users to move from conclusion back to evidence.
For example:
Insight → Study → Relevant passage → Original source
This creates confidence.
If an AI system says that several studies report an outcome, the researcher should be able to inspect those studies.
If AI identifies a potential evidence gap, the user should be able to understand which searches or sources led to that conclusion.
Traceability also helps researchers challenge the system.
If the AI interpretation is incorrect, the source material provides a basis for correction.
Pienomial provides an AI-powered intelligence environment designed to help organisations work with complex information.
Its Knol AI capabilities can support AI-assisted knowledge discovery and analysis, while its broader platform approach can help organisations build connected intelligence workflows around enterprise information.
For life sciences organisations, Pienomial's life sciences solution is particularly relevant to research environments where scientific, clinical, and competitive information needs to be brought together.
The practical value is not simply faster document summarisation.
A connected AI environment can help teams discover relevant information, organise evidence, connect findings across sources, and spend more time on scientific interpretation.
Organisations evaluating literature review automation can measure more than direct labour savings.
Calculate how much expert time is spent searching, screening, extracting, and organising literature.
Measure how long it takes to move from a research question to a validated evidence set.
Identify how frequently teams repeat literature searches that have already been performed elsewhere.
Measure the time required to refresh existing reviews with newly published research.
Estimate whether evidence-generation timelines delay clinical, scientific, portfolio, or strategic decisions.
AI should not automatically replace every manual review.
Manual approaches can remain appropriate when:
The evidence set is very small
The research question is highly specialised
The review requires unusual scientific judgment
The project has unique methodological requirements
The cost of automation exceeds the expected benefit
A pharmaceutical organisation implementing AI should establish clear controls.
Specify the population, intervention, comparator, outcomes, and other relevant criteria.
Determine which databases and documents can be used.
Decide which activities AI can assist with and which require human review.
Ensure extracted findings remain connected to their evidence.
Require expert review for conclusions that influence important decisions.
Evaluate whether the system is producing useful and accurate results over time.
Maintain appropriate records of searches, evidence, decisions, and revisions.
This approach allows organisations to gain efficiency without sacrificing scientific rigour.
The biggest benefit of literature automation may not appear in a productivity spreadsheet.
It may appear in decision quality.
When teams can review evidence faster, they can potentially:
Explore more alternatives
Identify evidence gaps earlier
Challenge assumptions sooner
Detect emerging scientific trends
Improve trial design
Respond faster to new findings
Support portfolio decisions with current evidence
The future is unlikely to eliminate human researchers from literature review.
Instead, the workflow is likely to become increasingly collaborative between humans and AI.
AI will handle more of the information-processing burden:
Discover → Filter → Extract → Connect → Monitor
Researchers will increasingly focus on:
Validate → Interpret → Challenge → Decide
This division of labour is particularly valuable in drug development because scientific expertise remains essential even when information processing becomes automated.
The goal is not to ask AI to replace scientific reasoning.
It is to make scientific reasoning more productive.
The hidden cost of manual literature reviews in drug development extends far beyond the number of hours spent searching databases and screening papers.
Manual processes can consume expert scientific capacity, slow evidence generation, create duplicated research, make evidence updates difficult, and limit how quickly teams can respond to emerging scientific developments.
An AI research platform can address these challenges by accelerating discovery, screening, extraction, evidence mapping, and literature monitoring.
Systematic literature review automation can reduce repetitive workload while keeping researchers involved in important inclusion, interpretation, and validation decisions.
A scientific literature AI tool can also help researchers navigate large evidence collections more efficiently, provided that outputs remain connected to credible source material.
For pharmaceutical organisations, the opportunity is therefore not simply to complete literature reviews faster.
It is to create a more continuous, connected, and evidence-driven research workflow.
Pienomial can support this transition by bringing AI-powered knowledge discovery and intelligence capabilities into life sciences research workflows.
The ultimate value of AI in literature review is measured not by how many papers a system can process.
It is measured by how much more effectively scientists can find the right evidence, understand it, challenge it, and use it to make better-informed drug development decisions.
[1] National Library of Medicine. PubMed. PubMed provides access to more than 40 million citations and abstracts covering biomedical literature and is a core resource for biomedical evidence discovery.
PubMed
[2] U.S. Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML) in Drug Development. FDA provides information on the use of AI and machine learning across drug development and its evolving regulatory considerations.
FDA — AI/ML in Drug Development
[3] European Medicines Agency. Artificial Intelligence. EMA outlines its work and considerations concerning artificial intelligence in medicines regulation.
EMA — Artificial Intelligence
[4] PRISMA Statement. PRISMA 2020 Statement. PRISMA provides reporting guidance intended to improve the transparency and completeness of systematic reviews and meta-analyses.
PRISMA 2020 Statement
[5] Cochrane. Cochrane Handbook for Systematic Reviews of Interventions. The handbook provides methodological guidance for conducting systematic reviews of interventions, including searching, study selection, data collection, and analysis.
Cochrane Handbook
