By Dr. Pravin D. Badhe | Founder & CEO, Swalife Biotech
Introduction: A Dashboard Is Not Evidence
Artificial intelligence is changing how pharmaceutical and biotechnology researchers discover information, interpret scientific literature, identify biological targets and prioritise development opportunities.
Modern research intelligence dashboards can bring together thousands of publications, molecular interactions, biological pathways, clinical studies and regulatory documents.
Within minutes, researchers can receive a structured overview of a disease, potential therapeutic targets, candidate molecules and research opportunities.
But an important question remains:
Can we trace every scientific conclusion displayed on the dashboard back to its original evidence?
An AI-generated statement may appear convincing. However, without identifying its source, experimental context, analytical method and limitations, it may not be reliable enough to guide an R&D decision.
This is why evidence provenance should be a foundational requirement of AI-enabled research intelligence.
1. What Is Evidence Provenance?
Evidence provenance describes the origin, history, processing and interpretation of scientific information.
It helps researchers understand not only what information is presented, but also where it came from and how it was produced.
For example, imagine an R&D dashboard displaying the statement:
“A plant-derived molecule may influence a biological pathway associated with cancer progression.”
Before using this information to guide research, scientists should be able to answer:
- Which scientific publication supports the association?
- Was the evidence generated in a computational, cell-based, animal or human study?
- Which compound, concentration, biological model and outcome were investigated?
- Was the mechanism directly demonstrated or inferred?
- Have independent studies reproduced the findings?
- Was the conclusion generated by an AI system or verified by a scientific reviewer?
These questions distinguish scientific evidence from an unsupported interpretation.
The World Wide Web Consortium’s PROV framework provides a structured approach to recording the entities, activities and agents involved in producing information.
For pharmaceutical R&D, provenance should extend beyond citations to include the experimental and computational processes underlying scientific conclusions.
2. Why Conventional AI Dashboards Can Be Misleading
AI-powered research tools can help summarise complex scientific information, but their outputs require critical evaluation.
A. A Citation Does Not Automatically Support a Claim
A research dashboard may display a scientific reference beside a conclusion.
However, the reference might report a different biological system, experimental condition or outcome.
Researchers must verify that the cited evidence genuinely supports the displayed statement.
B. Computational Predictions Are Not Experimental Findings
Machine learning, molecular docking, network pharmacology and other computational techniques can help generate research hypotheses.
However, predicted molecular interactions should not be presented as experimentally validated biological activity.
Similarly, a predicted binding affinity should not be confused with a measured binding value.
C. Evidence From Different Research Stages Is Not Interchangeable
An effect observed in cultured cells cannot automatically be interpreted as a therapeutic benefit in humans.
Animal findings, clinical observations, systematic reviews and regulatory conclusions each answer different scientific questions.
Combining them without identifying their differences may create a misleading impression of certainty.
D. Scientific Evidence Changes
Research findings evolve as new studies become available.
A dashboard prepared using older literature may overlook more recent contradictory findings, methodological limitations or changes in regulatory understanding.
Consequently, evidence provenance also requires version history and an identifiable date of assessment.
3. Evidence Provenance and the FAIR Data Principles
The FAIR Guiding Principles, introduced in 2016, provide an important foundation for scientific data management.
FAIR represents four characteristics:
Findable: Research information and supporting data should be discoverable through suitable identifiers and metadata.
Accessible: Researchers should understand how to retrieve the relevant information, including when access restrictions apply.
Interoperable: Data should use consistent representations and terminology so that information from different systems can be integrated.
Reusable: Scientific information should contain sufficient context, provenance and documentation to support appropriate reuse.
For an R&D intelligence dashboard, these principles can inform the design of evidence repositories, research workflows and scientific reporting systems.
FAIR compliance alone does not establish scientific validity. Nevertheless, FAIR-aligned documentation can make evidence easier to examine, compare and reuse.
4. What Should an Evidence-Traceable R&D Dashboard Contain?
I believe a scientifically responsible R&D intelligence dashboard should provide at least six essential layers of information.
Layer 1: Source Identification
Every significant scientific claim should be connected to an identifiable source.
Relevant metadata may include publication title, authors, publication year, DOI, database identifier or regulatory document reference.
Layer 2: Experimental Context
Researchers should know whether the underlying evidence originates from computational modelling, laboratory experiments, animal studies, clinical investigations or other sources.
Where relevant, the dashboard should record the experimental model, method, concentration, dose and measured endpoints.
Layer 3: Evidence Classification
Research findings should be categorised according to the type of evidence supporting them.
A computational hypothesis should remain clearly distinguishable from an experimentally demonstrated finding.
Layer 4: Interpretation and Limitations
Dashboards should identify uncertainty, contradictory observations, methodological weaknesses and gaps requiring further research.
Layer 5: AI Processing History
When AI systems are used to extract or interpret scientific information, the workflow should document the model or software version, processing date, relevant analytical settings and any transformations applied.
Layer 6: Human Scientific Review
Expert reviewers should evaluate whether the information is accurately extracted, appropriately interpreted and suitable for its intended research use.
A dashboard becomes more useful for decision-making when its conclusions can be independently examined—not simply when it contains more information.
5. Evidence Provenance in Natural-Product Drug Discovery
Natural-product research presents a particularly important application of evidence provenance.
Medicinal plants contain chemically diverse constituents, and the properties of an extract may depend on its botanical source, extraction process, composition and experimental conditions.
Consider a hypothetical research project investigating green tea-derived compounds for potential chemopreventive applications.
A research intelligence dashboard might include information about:
- The botanical source and extraction procedure.
- Identified constituents and their chemical structures.
- Published biological activity associated with individual compounds.
- Potential targets and pathways suggested by computational analysis.
- Available preclinical and clinical evidence.
- Formulation-related information and limitations.
- Safety, regulatory and translational considerations.
Importantly, these findings must remain connected to their original research context.
For example, biological activity reported for a purified compound should not automatically be attributed to a crude extract or a finished formulation.
Likewise, evidence of activity in an experimental cancer model should not be interpreted as established clinical cancer prevention.
An evidence-traceable system helps preserve these distinctions.
6. Connecting Evidence Provenance With AI-Assisted Drug Discovery
The potential value of AI in drug discovery extends beyond accelerating literature searches.
AI may assist researchers with identifying relevant publications, recognising relationships between molecular structures and biological targets, comparing evidence, generating hypotheses and preparing structured research reports.
However, these capabilities should operate within clearly defined scientific boundaries.
In January 2025, the U.S. Food and Drug Administration issued draft guidance proposing a risk-based credibility assessment framework for AI models used to generate information supporting regulatory decisions about drug and biological product safety, effectiveness or quality.
The framework emphasises evaluating AI credibility within a specific context of use.
Although that draft guidance is not a universal standard for every research dashboard, the underlying principle is relevant: an AI output must be assessed in relation to the decision it is intended to support.
An exploratory research hypothesis and a regulatory submission require different levels of assurance.
7. From Research Intelligence to Product Development Decisions
A well-designed research intelligence dashboard should support practical questions throughout the product-development process.
For example:
Research planning: Which scientific questions remain unanswered?
Target identification: What evidence supports the association between a molecule and a biological target?
Candidate prioritisation: What experimental evidence and development constraints should influence candidate selection?
Formulation development: Are there solubility, stability, bioavailability or safety concerns that require investigation?
Translational research: What additional studies would be necessary before advancing a candidate?
Regulatory planning: What evidence may be required to support the intended product category and proposed claims?
These decisions should not rely solely on a dashboard’s graphical presentation.
They require transparent supporting information, expert interpretation and, where necessary, experimental validation.
8. Our Approach at Swalife Biotech: Product Development & Medical Intelligence
At Swalife Biotech, our research direction involves integrating scientific evidence, AI-assisted analysis and structured product-development intelligence.
Through our Product Development & Medical Intelligence (PDMI) initiative, we are working towards research workflows that connect information across multiple stages of pharmaceutical and botanical product development.
Our approach emphasises:
- Traceable scientific evidence and source verification.
- Structured target and pathway research.
- Molecular and formulation intelligence.
- Clear distinctions between predictions and validated findings.
- Scientific review before information is used to support development decisions.
- Translation of research findings into practical development questions.
For natural-product research, this approach is particularly relevant to botanical formulations, nutraceutical development, precision chemoprevention research and the investigation of plant-derived small molecules.
We see evidence provenance as an important design requirement for these workflows, rather than claiming that every component has already undergone independent validation.
Our objective is to support researchers and development teams in moving from fragmented scientific information towards more transparent, evidence-informed decisions.
9. A Practical Checklist for R&D Teams
Before relying on an AI-generated research intelligence report, consider the following:
- Can the original evidence supporting each important conclusion be retrieved?
- Are computational predictions clearly identified?
- Are preclinical and clinical findings distinguished?
- Are conflicting studies and limitations documented?
- Are source dates, versions and processing methods recorded?
- Has a qualified scientific reviewer assessed the interpretation?
- Is the information appropriate for the intended R&D decision?
If a dashboard cannot answer these questions, its output should be treated as preliminary research intelligence rather than a validated scientific conclusion.
Conclusion: The Future of Research Intelligence Must Be Traceable
The future of pharmaceutical R&D will not be determined simply by how quickly AI systems generate reports or how attractively information is presented.
The more important question is whether researchers can evaluate, verify and appropriately use the information they receive.
AI can help us process scientific information faster. Evidence provenance helps us understand whether that information deserves our confidence.
As AI-enabled research platforms evolve, traceability, scientific accountability, human review and appropriate validation should become central to their design.
For research-driven biotechnology companies, these principles are essential to building more credible and useful product-development intelligence.
Explore Swalife Research Intelligence
Swalife Biotech is developing AI-supported research and product-development intelligence approaches for botanical, nutraceutical and pharmaceutical R&D.
Explore our research platforms and scientific capabilities:
Predictive Intelligence: https://predictiveintelligence.swalifebiotech.com/
Research Intelligence: https://swalifeintelligence.swalifebiotech.com/
Swalife Healthcare Consultancy: https://www.swalifehealthcare.com/
Swalife Biotech: https://www.swalifebiotech.com/
This article is intended for scientific education and research discussion. Computational predictions and preliminary research findings should not be interpreted as established clinical efficacy, safety or regulatory approval.
References
- Wilkinson MD, et al. (2016). The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
- World Wide Web Consortium (2013). PROV-Overview: An Overview of the PROV Family of Documents. https://www.w3.org/TR/prov-overview/
- U.S. Food and Drug Administration (2025). Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Draft Guidance. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
- U.S. Food and Drug Administration. Quality Essentials: Inspectional Coverage of QMS and Data Integrity. https://www.fda.gov/media/179075/download