Precision medicine spans genomics, pharmacogenomics, molecular diagnostics, and data‑driven tailoring of therapy. In 2026, WHO has explicitly engaged with precision medicine’s role in health systems, while multiple scientific and policy papers highlight persistent gaps between scientific potential and real‑world implementation [1][2][3][4].

Major Evidence Gaps

1. Under‑representation of Diverse Ancestries in Genomic and Pharmacogenomic Data

Problem

• Large genomic and pharmacogenomic datasets remain heavily skewed toward populations of European ancestry.

• Recent analyses and initiatives in Africa and other under‑represented regions emphasize that this gap leads to misinterpreted variants, inaccurate risk predictions, and potentially inappropriate therapy choices for many populations [2][5].

Consequences

• Polygenic risk scores and certain pharmacogenomic markers perform poorly or unpredictably in non‑European ancestries.

• Health disparities risk being amplified as precision tools work best for already well‑served groups.

2. Limited Real‑World Outcomes and Cost‑Effectiveness Data

• Many precision medicine interventions are supported primarily by:

• Surrogate or intermediate endpoints.

• Small, selected trial populations.

• There is a shortage of robust data on:

• Long‑term clinical outcomes in routine practice.

• Cost‑effectiveness and budget impact across health systems.

Implications

• Payers and guideline panels face uncertainty about which genomic and pharmacogenomic tests truly improve outcomes relative to cost.

• This constrains coverage and slows implementation.

3. Integration of Clinical Genomics and Pharmacogenomics into Routine Care

Recent commentaries highlight a “genomics–pharmacogenomics gap”, where clinical genomics (e.g., tumor sequencing, rare disease diagnostics) and pharmacogenomics are developed and deployed in silos, with little integrated workflow [1][3].

Evidence gaps

• When and how to combine somatic, germline, and pharmacogenomic data in a coherent care pathway.

• Effective team models (genetic counselors, pharmacists, primary care, specialists) that translate complex profiles into concrete prescribing decisions.

4. Implementation Science: What Actually Works in Real Health Systems?

• Many precision medicine pilots occur at well‑resourced academic centers with bespoke infrastructure, but:

• There is limited evidence on scalable models in community hospitals, primary care networks, and low‑ and middle‑income countries.

• We lack comparative effectiveness studies of different implementation strategies (e.g., point‑of‑care testing vs centralized labs; pharmacist‑led vs clinician‑led pharmacogenomics).

5. AI‑Enabled Precision Medicine: Validation and Generalizability

• AI models for imaging, risk prediction, and treatment selection are proliferating, but real‑world reviews emphasize substantial gaps in external validation, transparency, and bias evaluation [4].

Key questions:

• How do AI‑driven tools perform across sites, devices, and populations different from those on which they were trained?

• What governance and monitoring frameworks best ensure safety and equity?

6. Ethical, Legal, and Social Implications (ELSI) in Diverse Settings

• WHO notes that precision medicine raises concerns about privacy, data sharing, consent, and equitable benefit [1].

• There is insufficient empirical evidence on:

• How different communities perceive genomic data use.

• What consent, return‑of‑results, and benefit‑sharing approaches build trust, particularly where historical exploitation exists.

Counterarguments

• Some argue that given the pace of progress, evidence gaps are a natural lag and will close over time. While true to a point, several gaps (ancestral diversity, implementation in low‑resource settings) are structural and will persist without targeted investment.

• Others contend that economics should take precedence over more trials; yet HTA decisions require precisely the cost‑effectiveness and outcomes data that are currently missing.

Priority Actions to Address Gaps

1. Fund large, ancestrally diverse cohorts and biobanks tightly linked to electronic health records, including in Africa, Latin America, South Asia, and indigenous populations [2][5][6].

2. Embed pragmatic trials and registries into clinical care to gather outcome and cost‑effectiveness evidence on genomic and pharmacogenomic interventions.

3. Develop interoperable, integrated CDS tools that combine genomic, pharmacogenomic, and clinical data, aligned with FDA’s CDS guidance and WHO’s precision medicine principles [7][8][1].

4. Standardize evaluation of AI models (transparent performance reporting, external validation, fairness/bias assessment) in precision medicine contexts [4].

5. Co‑design ELSI frameworks with communities, ensuring that precision medicine initiatives incorporate local priorities and governance structures.