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    Perpetuals (NASDAQ: PDC) Extends BayesShield AI Into Healthcare to Address the Root Cause of Diagnostic Errors

    4/22/26 8:00:00 AM ET
    $PDC
    EDP Services
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    Get the next $PDC alert in real time by email
    • Proprietary algorithm, originally built to predict trading outcomes, now targets the millions of patients harmed annually by diagnostic errors
    • European Institute of Management to support clinical implementation with partner hospitals through its PhD in Healthcare Studies program

    Perpetuals.com Ltd (NASDAQ:PDC), the AI-powered financial services company, today announced the extension of its proprietary BayesShield AI technology into healthcare. BayesShield Clinical applies the same patent-pending methodology the company developed to predict trading outcomes to a new domain: identifying which physicians in hospitals and clinics are the most accurate at diagnosing specific types of cases to ensure patients get the best health outcomes.

    In the United States alone, an estimated 795,000 patients die or are permanently disabled each year due to diagnostic errors (Johns Hopkins / BMJ Quality & Safety, 2024). Cognitive biases have been associated with diagnostic inaccuracies in as many as 77% of case scenarios across systematic review studies (BMC Medical Informatics and Decision Making, 2016), and between 80% and 90% of physicians openly acknowledge practicing defensive medicine (University of Wisconsin / EQ, 2024) by ordering unnecessary tests and referring conservatively to avoid malpractice risk, which obscures their true diagnostic skill. The result is a healthcare system that spends an estimated $210 billion annually on unnecessary medical services (National Academy of Medicine, 2012) while failing to identify and leverage its best diagnosticians.

    BayesShield Clinical is designed for hospitals, clinics, and health systems. It does not diagnose patients or replace physician judgment. Instead, it strips away behavioral biases, applying statistical methods to identify patterns of diagnostic strength across specific case types. Hospitals and other health care providers can use these insights for optimized case routing, second-opinion protocols, and training program design. Financial markets and clinical medicine share a common analytical problem: decisions made under uncertainty, where skill is obscured by noise, variable conditions, and behavioral bias. The company believes the same approach that identifies performance patterns in financial markets can reveal them in clinical decision-making.

    "We built BayesShield to find why traders lose money, hidden under layers of behavioral noise. The same methodology works in other high-stakes decisions, including healthcare," said Patrick Gruhn, CEO of Perpetuals. "The AI platform we developed helps hospitals identify which of their physicians have statistically exceptional diagnostic patterns in specific case types, so the patient gets the best care by getting the right diagnosis from the outset."

    Perpetuals has partnered with the European Institute of Management (EIM), a higher education institution offering doctoral programs, including a PhD in Healthcare Studies, to support the clinical implementation of BayesShield Clinical. Under the arrangement, Perpetuals provides the software platform while EIM works directly with partner hospitals and clinics to manage pilot deployment, trial design, and academic oversight. EIM's PhD in Healthcare Studies program will serve as the academic collaboration for the clinical pilots, with doctoral candidates contributing to the study design and analysis of outcomes. Additional hospital, health system, and research partnerships are in development as the company prepares for broader deployment.

    "We are grateful to partner with Perpetuals and explore how AI systems can improve diagnostics in clinical settings," said Prof. Dr. Michael Neubert, PhD, Founder of EIM. "Joint efforts like this advance evidence-based innovation in healthcare while maintaining a clear distinction from therapeutic interventions."

    BayesShield was originally developed and stress-tested in financial markets, widely considered the most adversarial, data-rich, and bias-prone decision environment. The platform is trained on over 11.7 billion actual retail trades sourced from larger derivatives trading venues.

    About Perpetuals

    Perpetuals.com Ltd (NASDAQ:PDC) is a fintech company developing AI-powered trading products and prediction markets, with a global footprint across the United States, Europe, and Asia. Its mission is to reduce risk through empowering retail users with intuitive, secure, and efficient trading experiences across multiple asset classes. The company is extending its AI methodology to healthcare and other decision-intensive domains through BayesShield.

    Perpetuals's proprietary trading platform, Kronos X, combines advanced AI and data analysis. The technology is trained on billions of trades, monitors market activity in real time, identifies patterns for trading and risk decisions, and provides multi-asset coverage with self‑clearing blockchain-based settlement. The company's licensed European Multilateral Trading Facility (MTF) infrastructure and Kronos X multi‑asset exchange platform operate with full MiFID II, MiCA, DORA, and EMIR compliance. Perpetuals CEO Patrick Gruhn is a Professor and PhD candidate for computer science and engineering, and founder of the Patrick Gruhn Institute at the EIM.

    For more information, visit www.perpetuals.com.

    About the European Institute of Management

    European Institute of Management is a higher education and executive learning institution specializing in advanced research, digital transformation, and leadership development. The institute applies rigorous research methodologies and interdisciplinary approaches to address complex challenges at the intersection of management, technology, and healthcare. EIM collaborates with academic, clinical, and industry partners to develop and evaluate evidence-based solutions, including the testing of AI systems in healthcare settings.

    Forward-Looking Statements: This press release contains forward-looking statements as defined within the meaning of the Private Securities Litigation Reform Act of 1995, including, but not limited to, statements set forth in the company's filings with the Securities and Exchange Commission. Words such as "expect", "will", "believes", "positions", "advancing", "aligning", and other similar expressions may indicate forward-looking statements, though not all forward-looking statements contain such words. These statements reflect the company's current view with respect to future events, are subject to risks and uncertainties that could cause actual results to differ materially, including regulatory approvals, successful pilot deployment, trial design, trial completion, failure to produce validated trial results, market conditions, and risks detailed in the company's SEC filings, and are necessarily based upon a number of estimates and assumptions that, while considered reasonable by the company, are inherently subject to significant business, economic, competitive, political, and social uncertainties, and contingencies. Should one or more of these risks or uncertainties materialize, or should the assumptions set out by the company underlying those forward-looking statements prove incorrect, actual results may vary materially from those described herein. Individuals are cautioned that forward-looking statements are not guarantees of future performance and accordingly investors are cautioned not to put undue reliance on forward-looking statements due to the inherent uncertainty therein. These forward-looking statements are made as of the date of this press release and the company does not intend, and does not assume any obligation, to update these forward-looking statements, except as required by law.

    View source version on businesswire.com: https://www.businesswire.com/news/home/20260422859218/en/

    Media Contact:

    press@perpetuals.com

    Investor Relations:

    ir@perpetuals.com

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