Jose Pereira Leal is a prolific author at Linked In:
https://www.linkedin.com/in/pereiraleal/
See e.g. his June essay, "Lessons from the validation trenches of biomarker development,"
https://www.linkedin.com/pulse/from-promising-proven-lessons-validation-trenches-jose-pereira-leal-jh2ff/
He has a new pair of articles, "Rebuilding the Case for Diagnostics Investment," parts 1 and 2.
Part 1
Part 2
Part 1 opens like this:
The diagnostics sector faces a paradox: we're living through a precision medicine revolution, yet the tools that make precision medicine possible remain chronically underfunded.
In 2023-2024, diagnostics captured just $2.2 billion in global venture funding, while AI companies raised over $100 billion globally in 2024. Even within healthcare, nearly 30% of the $23 billion in US healthcare VC funding went to AI-focused startups, leaving diagnostics fighting for scraps.
Why does the market avoid a sector that underpins every major advance in modern medicine?
The Perfect Storm of Disincentives....
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He is also author of the book, Precision Diagnostics [views from the trenches].
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AI CORNER
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Chat GPT 5 summarizes "Building the Case - Investments in Dx" Parts 1 & 2.
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Below is a ~400-word integrated summary of both essays for graduate-level readers of your diagnostics strategy blog, synthesizing Part 1 (entrepreneur perspective) and Part 2 (investor perspective).
Summary (≈400 words)
The two essays argue that diagnostics are systematically undervalued, not because they generate weak economic returns, but because entrepreneurs and investors apply frameworks borrowed from biotech, SaaS, and medtech that misinterpret the category’s timelines, value drivers, and risk profile. Diagnostics occupy an unusual middle ground: development cycles of 7–10 years, regulatory complexity (IVDR, FDA, CLIA), and reimbursement friction make them slow to scale, yet their commercial upside rarely achieves drug-like revenue multiples. Because venture models are calibrated for either hyper-scaling software or blockbuster therapeutics, diagnostics appear to suffer from “ugly duckling” economics.
The author argues this perception is outdated. Precision medicine is producing captive markets for companion diagnostics, validated datasets create defensible assets, regulatory complexity functions as a barrier to entry, and high-value diagnostics can support premium reimbursement when positioned as clinical decision infrastructure rather than commodity testing. Additionally, risk-adjusted returns compare favorably to pharma: diagnostics require 1/10 the capital and offer higher hit rates with shorter cycles to revenue, even if individual exits are smaller. This creates a genuine arbitrage opportunity for disciplined capital.
Part 1 offers strategic guidance for founders: build with pharma rather than parallel to it; treat data as the product, not exhaust; embrace regulatory barriers as moat; price based on clinical and economic impact, not cost-per-test; and build platforms that enable expansion of indications and revenue streams. The central thesis is that diagnostic businesses should be understood as infrastructure for precision therapeutics, rather than isolated products.
Part 2 shifts to an investor framework, arguing conventional staging models fail because they focus on validation checkpoints rather than business model de-risking. The standard linear progression—analytical validation, clinical trials, regulatory approval, commercialization—ignores the much harder work of market adoption, including guideline inclusion, physician behavior change, reimbursement, workflow integration, and payer contracting.
The author proposes diagnostics-specific evaluation criteria based on explicit business model archetypes (single-test, platform, data-as-product, companion diagnostic) and on identifying and de-risking 3–4 core assumptions that determine value creation. Investors should intentionally structure portfolios to leverage technology, data, commercial, and evidence-sharing synergies, and provide ecosystem-level capabilities in regulatory strategy, clinical evidence architecture, payer engagement, pharma partnerships, and health-system integration.
Ultimately, the essays contend that diagnostics are mispriced assets, not weak assets, and that founders and investors who adopt category-specific strategies can exploit a major market inefficiency as precision medicine becomes standard of care.
If you’d like: a shorter “executive version,” a more polemical version for Substack, or a version with takeaway bullets for board presentations, I can tailor it.
