References & Scholarly Resources

Precision medicine (foundations)

- National Cancer Institute (NCI). “Precision medicine (definition).”

Annotation: Clear, public-facing definition of precision medicine and how oncology uses tumor-specific information for diagnosis, treatment planning, and prognosis.

https://www.cancer.gov/publications/dictionaries/cancer-terms/def/precision-medicine

- National Institutes of Health (NIH). “The Promise of Precision Medicine.”

Annotation: NIH overview explaining what precision medicine is, why it matters, and how it’s shaped by genetics, environment, and lifestyle.

https://www.nih.gov/about-nih/nih-turning-discovery-into-health/promise-precision-medicine

Why precision medicine “works” in cancer (and what that teaches us)

- U.S. Food & Drug Administration (FDA). “Companion Diagnostics.”

Annotation: Defines companion diagnostics and explains why matching a therapy to a biomarker is essential for safe/effective targeted treatment.

https://www.fda.gov/medical-devices/in-vitro-diagnostics/companion-diagnostics

- U.S. Food & Drug Administration (FDA). “List of Cleared or Approved Companion Diagnostic Devices (In Vitro and Imaging Tools).”

Annotation: Concrete evidence that biomarker-guided therapy is operationalized in oncology—this is what a mature precision ecosystem looks like.

https://www.fda.gov/medical-devices/in-vitro-diagnostics/list-cleared-or-approved-companion-diagnostic-devices-in-vitro-and-imaging-tools

Precision psychiatry roadmaps (where the field is heading)

- Kas, M. J. H., et al. (2025). “Precision psychiatry roadmap: towards a biology-informed…” (open-access, PMC).

Annotation: Modern roadmap arguing for biology-informed psychiatric classification and the research/clinical infrastructure needed to get there.

https://pmc.ncbi.nlm.nih.gov/articles/PMC12240818/

- Kas, M. J. H., et al. (2025). “Towards a consensus roadmap for a new diagnostic framework for mental disorders.” (ScienceDirect).

Annotation: Stakeholder-driven consensus principles for moving beyond traditional nosology toward continuously improving precision diagnostics.

https://www.sciencedirect.com/science/article/pii/S0924977X24007168

- Williams, L. M. (2024). “Precision psychiatry and Research Domain Criteria (RDoC): implications for clinical trials and future practice.” CNS Spectrums.

Annotation: Connects transdiagnostic RDoC thinking to trial design and future implementation—useful for explaining “mechanism-first” thinking.

https://www.cambridge.org/core/journals/cns-spectrums/article/precision-psychiatry-and-research-domain-criteria-implications-for-clinical-trials-and-future-practice/C0DE02C053C0B85E411B57449E1F8BA4

Beyond DSM labels: research frameworks for mechanism-first science

- Insel, T. R. (2014). “The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry.” American Journal of Psychiatry.

Annotation: Landmark piece stating the long-term goal: build psychiatric classification around biobehavioral systems rather than symptom checklists.

https://psychiatryonline.org/doi/10.1176/appi.ajp.2014.14020138

- Cuthbert, B. N. (2022). “Research Domain Criteria (RDoC): Progress and Potential.” (open-access, PMC).

Annotation: Updated overview of what RDoC is (and isn’t), plus practical progress and limitations—great for setting expectations.

https://pmc.ncbi.nlm.nih.gov/articles/PMC9187047/

- Haywood, D., Castle, D. J., & Hart, N. H. (2024). “Avoiding the pitfalls of the DSM-5: A primer for health professionals.” General Hospital Psychiatry.

Annotation: Short, clinically oriented critique highlighting common diagnostic pitfalls and why symptom-based systems can mislead care and research.

https://pubmed.ncbi.nlm.nih.gov/39053381/

Actionable precision today in psychiatry: pharmacogenomics (real but narrower than oncology)

- Bousman, C. A., et al. (2023). CPIC Guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4, HTR2A and serotonin reuptake inhibitor antidepressants. (open-access, PMC).

Annotation: Practical, evidence-graded prescribing recommendations; also clarifies where evidence is insufficient (important for credibility).

https://pmc.ncbi.nlm.nih.gov/articles/PMC10564324/

- CPIC. “Guideline for SSRI/SNRI antidepressants (April 2023) – landing page.”

Annotation: Easy-to-navigate home for updates, tables, and implementation notes; useful for clinicians and collaborators.

https://cpicpgx.org/guidelines/cpic-guideline-for-ssri-and-snri-antidepressants/

The public health burden: mental illness + treatment gaps (U.S.)

- National Institute of Mental Health (NIMH). “Mental Illness” statistics page.

Annotation: Authoritative prevalence estimates and treatment rates; useful for framing the scale of unmet need.

https://www.nimh.nih.gov/health/statistics/mental-illness

- SAMHSA. “Key Substance Use and Mental Health Indicators in the United States: Results from the 2024 NSDUH” (PDF).

Annotation: Best single U.S. federal report for substance use + mental health + treatment indicators in one place.

https://www.samhsa.gov/data/sites/default/files/reports/rpt56287/2024-nsduh-annual-national-report.pdf

The overdose epidemic: primary data sources

- CDC (NCHS). “Provisional Drug Overdose Death Counts” dashboard.

Annotation: Official provisional (near-real-time) overdose death estimates; cite for trends and current 12-month periods.

https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

- CDC Overdose Prevention. “Data Resources / Facts & Stats” (updated Jan 2026).

Annotation: CDC summary page that states the latest preliminary 12-month estimate ending Aug 2025 and percent change—good for your blog’s “current state” stats.

https://www.cdc.gov/overdose-prevention/data-research/facts-stats/index.html

- CDC Media Release (Feb 25, 2025). “CDC Reports Nearly 24% Decline in U.S. Drug Overdose Deaths…”

Annotation: Clear, quotable CDC statement describing the decline from ~114,000 to ~87,000 (Oct 2023–Sep 2024) and how provisional counts work.

https://www.cdc.gov/media/releases/2025/2025-cdc-reports-decline-in-us-drug-overdose-deaths.html

Why ‘AI/ML will fix psychiatry’ is not automatic (and what good science requires)

- Chekroud, A. M., et al. (2024). “Illusory generalizability of clinical prediction models.” Science.

Annotation: Major warning shot: prediction models can look strong inside one context and fail elsewhere—external validation is non-negotiable.

https://www.science.org/doi/10.1126/science.adg8538

- Ostojic, D., et al. (2024). “The challenges of using machine learning models in psychiatry.” European Psychiatry (ScienceDirect).

Annotation: Practical checklist of pitfalls—data quality, bias, validation, interpretability—useful for explaining your rigor-first stance.

https://www.sciencedirect.com/science/article/pii/S0924977X24001974

- Sun, J., et al. (2025). “Practical AI application in psychiatry: historical review and…” Molecular Psychiatry.

Annotation: High-level review of what’s promising, what’s hype, and why real-world deployment is still limited.

https://www.nature.com/articles/s41380-025-03072-3

Neuroinflammation (a key mechanistic axis linking stress, immune signaling, and symptoms)

- Miller, A. H., & Raison, C. L. (2016). “The role of inflammation in depression.” JAMA Psychiatry.

Annotation: Seminal review explaining how inflammatory signaling interfaces with brain circuits and treatment response/resistance.

https://pubmed.ncbi.nlm.nih.gov/26711676/

- Hassamal, S., et al. (2023). “Chronic stress, neuroinflammation, and depression.” Frontiers in Psychiatry.

Annotation: Mechanistic overview connecting stress physiology to immune activation and depressive phenotypes; widely cited and accessible.

https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2023.1130989/full

- Beurel, E., Toups, M., & Nemeroff, C. B. (2020). “The Bidirectional Relationship of Depression and Inflammation.” Neuron.

Annotation: Strong synthesis for explaining feedback loops: inflammation can drive depression and depression can amplify inflammatory states.

https://www.cell.com/neuron/fulltext/S0896-6273%2820%2930431-1