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QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

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QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

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QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

Nine instruments validated against published standards including Columbia C-SSRS, CIWA-Ar, and COWS; paper-only workflow showed materially lower detection rates across the same scenarios; 60-day live pilot structured and ready

Toronto, Ontario–(Newsfile Corp. – May 4, 2026) – QScreen AI Inc. (CSE: QAI) (OTC Pink: PMEDF) (FSE: 3QP), an innovator fusing Quantum-AI technologies to transform health screening today reports internal validation results for its correctional intake platform across 65 calibrated clinical scenarios tested against published instrument thresholds.

By digitizing the high-liability blind spots of correctional intake, QAI transforms a universal institutional risk into a scalable SaaS revenue model with zero-hardware deployment friction. Prior announcements described what this platform is capable of and today’s announcement reports what it achieved.

What the Comparison Shows

We ran the same 40 clinical scenarios through a paper-only intake model built on published self-disclosure rates from the correctional health literature. The gap was significant. The paper-only model identified zero of the seven post-release overdose cases in the cohort. It identified zero of the suicidal patients who had not volunteered the information and it identified zero impairment cases where the only signal was physiological. QAI Correctional intake platform identified all of them.

That gap has a dollar figure attached to it. Post-release overdose deaths carry a documented societal cost exceeding one million dollars per incident, per published health economics literature. Seven identifiable cases per forty-booking cohort, each triggering a naloxone protocol and a MAT referral before the patient’s release date, is an intervention with real economic value at the one moment in the justice system where it is still possible to make it. It is also a liability question. Every one of those cases represents a documented risk that a paper form cannot defend in court. QAI creates the clinical record and every decision documented, every flag timestamped, every nurse override recorded with a rationale.

The Platform

QAI administers nine validated clinical instruments during a structured intake of under ten minutes on a standard laptop running a standard camera without a need for specialized hardware (no need for a multispectral camera) or an IT project. The instruments are CIWA-Ar (Sullivan 1989), COWS (Wesson & Ling 2003), Columbia C-SSRS (Posner 2011), PREA-R (Moss & Metzger 2009), MAT Readiness per SAMHSA TIP 63, post-release overdose risk per Binswanger (NEJM 2007), a benzodiazepine withdrawal clinical rule, a 72-hour deterioration model, and an AI clinical summary. The nine reported here are those validated in this study against published thresholds. The full 13-instrument architecture remains available for deployment.

Validation Methodology and Results

Scenario-based validation against published clinical thresholds is the recognized pre-deployment methodology for clinical decision support tools under ASME V&V 40-2018. Ground truth was set from published instrument thresholds before any scenario was run. Reporting follows ASME V&V 40-2018, STARD 2015, and CONSORT-Pilot 2016.

Suicide Risk – Columbia C-SSRS: 100% sensitivity, 100% specificity. Every patient with active ideation identified before housing assignment. Zero false alarms across 37 non-SI patients.

Withdrawal – CIWA-Ar and COWS: 100% sensitivity, 100% specificity. Every presentation including benzodiazepine withdrawal – a condition with no validated scale and no systematic flag in a paper-only intake process.

PREA-R Victim Risk: 100% sensitivity, 100% specificity. Every high-risk placement identified before general population assignment, consistent with 28 CFR 115.41.

Camera Fitness Clearance: 100% sensitivity, 100% specificity. Including two methamphetamine presentations standard PERCLOS screening would have cleared.

MAT Readiness: 89% sensitivity, 100% specificity.

Post-Release Overdose Risk: 88% sensitivity, 97% specificity. Seven CRITICAL cases per cohort, each generating naloxone protocol and MAT referral before release.

72-Hour Deterioration: 48% at ELEVATED tier, 100% specificity. No HIGH-acuity patient misrouted.

Camera physiological pilot – 25 scenarios: 92% clearance accuracy, 100% UNFIT sensitivity, 96% discrepancy detection.

Three Gaps Found and Corrected

A validation that finds nothing is not a validation. Before finalizing results, the team identified and corrected three gaps. The most common poly-drug intake response “multiple substances” was not triggering CIWA-Ar or COWS scoring; expanding the keyword matching raised withdrawal sensitivity from 75% to 100%. Benzodiazepine withdrawal was receiving no clinical flag because no validated scale exists for it; a seizure-precaution rule was built and added. A calibration error on one passive suicidal ideation case was identified and corrected against published C-SSRS thresholds, which moved suicide sensitivity from 67% to 100%. All three corrections are documented in full and available on request.

Commercial Context

Every correctional facility booking carries documented liability exposure when no structured clinical assessment is conducted. Suicide in custody is the leading cause of jail death in the United States. Withdrawal deaths are preventable and have been litigated. Post-release overdose mortality carries a societal cost that exceeds one million dollars per incident and is documented in the New England Journal of Medicine. These are not hypothetical risks and they show up on legal invoices.

This validation ties sensitivity and specificity data to published clinical standards across every one of those risk categories. A procurement officer, health director, or risk counsel reviewing it has a documented evidentiary basis for a deployment decision. The platform needs no hardware, no IT engagement, and no capital budget. Deployment follows upon facility onboarding and agreement execution.

The 60-day live pilot is structured, pre-specified, and ready. Two 30-day phases, parallel operation alongside standard paper intake, then independent operation with full nurse override authority throughout, converting to a 12-month SaaS agreement on meeting five pre-defined performance criteria. The platform is built, validated, and commercially structured. Facility discussions are active and the Company expects to provide an update in the near term.

Dr. Rahul Kushwah, COO of QScreen AI Inc., stated: “This is not a capabilities update. It is a performance report with published references behind every number. We ran the same cohort through a paper-only intake model and the detection gap was material across every clinical category. The facilities we talk to already know they have a liability problem. What this gives them is the clinical evidence to act on it, with no hardware requirement, no IT project, and a 60-day pilot that converts to contracted revenue on meeting pre-specified criteria.”

About QScreen AI Inc.

QScreen AI Inc. (CSE: QAI) (OTC Pink: PMEDF) (FSE: 3QP) is a health technology company building a proprietary artificial intelligence engine with quantum inspired computing and advanced physiological sensing to clinical and occupational health assessments across correctional facilities, addiction medicine rehabilitation, and industrial workforce screening in multiple jurisdictions.

For more information visit www.q-screen.ai.

Contact
Dr. Rahul Kushwah, COO
Rahul.kushwahphd@gmail.com
(647) 889 6916

Caution Regarding Forward-Looking Information:

This news release may contain forward-looking statements and information based on current expectations. The validation used simulated scenarios and synthetic patient data; live performance may differ materially. The platform is a clinical decision support tool requiring confirmation by a licensed healthcare professional before any action is taken. Although such statements are based on management’s reasonable assumptions, there can be no assurance that such assumptions will prove to be correct. We assume no responsibility to update or revise them to reflect new events or circumstances. The Company’s securities have not been registered under the U.S. Securities Act of 1933, as amended (the “U.S. Securities Act”), or applicable state securities laws, and may not be offered or sold to, or for the account or benefit of, persons in the United States or “U.S. Persons”, as such term is defined in Regulations under the U.S. Securities Act, absent registration or an applicable exemption from such registration requirements. This press release shall not constitute an offer to sell or the solicitation of an offer to buy nor shall there be any sale of the securities in the United States or any jurisdiction in which such offer, solicitation or sale would be unlawful.

QScreen AI screening tools provide risk assessment and decision support only. They are not diagnostic medical devices and are not intended to replace professional medical judgment.

THE CANADIAN SECURITIES EXCHANGE HAS NOT REVIEWED NOR DOES IT ACCEPT RESPONSIBILITY FOR THE ADEQUACY OR ACCURACY OF THIS RELEASE.

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/295679

View more news from QScreen AI Inc.

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QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

Nine instruments validated against published standards including Columbia C-SSRS, CIWA-Ar, and COWS; paper-only workflow showed materially lower detection rates across the same scenarios; 60-day live pilot structured and ready

Toronto, Ontario–(Newsfile Corp. – May 4, 2026) – QScreen AI Inc. (CSE: QAI) (OTC Pink: PMEDF) (FSE: 3QP), an innovator fusing Quantum-AI technologies to transform health screening today reports internal validation results for its correctional intake platform across 65 calibrated clinical scenarios tested against published instrument thresholds.

By digitizing the high-liability blind spots of correctional intake, QAI transforms a universal institutional risk into a scalable SaaS revenue model with zero-hardware deployment friction. Prior announcements described what this platform is capable of and today’s announcement reports what it achieved.

What the Comparison Shows

We ran the same 40 clinical scenarios through a paper-only intake model built on published self-disclosure rates from the correctional health literature. The gap was significant. The paper-only model identified zero of the seven post-release overdose cases in the cohort. It identified zero of the suicidal patients who had not volunteered the information and it identified zero impairment cases where the only signal was physiological. QAI Correctional intake platform identified all of them.

That gap has a dollar figure attached to it. Post-release overdose deaths carry a documented societal cost exceeding one million dollars per incident, per published health economics literature. Seven identifiable cases per forty-booking cohort, each triggering a naloxone protocol and a MAT referral before the patient’s release date, is an intervention with real economic value at the one moment in the justice system where it is still possible to make it. It is also a liability question. Every one of those cases represents a documented risk that a paper form cannot defend in court. QAI creates the clinical record and every decision documented, every flag timestamped, every nurse override recorded with a rationale.

The Platform

QAI administers nine validated clinical instruments during a structured intake of under ten minutes on a standard laptop running a standard camera without a need for specialized hardware (no need for a multispectral camera) or an IT project. The instruments are CIWA-Ar (Sullivan 1989), COWS (Wesson & Ling 2003), Columbia C-SSRS (Posner 2011), PREA-R (Moss & Metzger 2009), MAT Readiness per SAMHSA TIP 63, post-release overdose risk per Binswanger (NEJM 2007), a benzodiazepine withdrawal clinical rule, a 72-hour deterioration model, and an AI clinical summary. The nine reported here are those validated in this study against published thresholds. The full 13-instrument architecture remains available for deployment.

Validation Methodology and Results

Scenario-based validation against published clinical thresholds is the recognized pre-deployment methodology for clinical decision support tools under ASME V&V 40-2018. Ground truth was set from published instrument thresholds before any scenario was run. Reporting follows ASME V&V 40-2018, STARD 2015, and CONSORT-Pilot 2016.

Suicide Risk – Columbia C-SSRS: 100% sensitivity, 100% specificity. Every patient with active ideation identified before housing assignment. Zero false alarms across 37 non-SI patients.

Withdrawal – CIWA-Ar and COWS: 100% sensitivity, 100% specificity. Every presentation including benzodiazepine withdrawal – a condition with no validated scale and no systematic flag in a paper-only intake process.

PREA-R Victim Risk: 100% sensitivity, 100% specificity. Every high-risk placement identified before general population assignment, consistent with 28 CFR 115.41.

Camera Fitness Clearance: 100% sensitivity, 100% specificity. Including two methamphetamine presentations standard PERCLOS screening would have cleared.

MAT Readiness: 89% sensitivity, 100% specificity.

Post-Release Overdose Risk: 88% sensitivity, 97% specificity. Seven CRITICAL cases per cohort, each generating naloxone protocol and MAT referral before release.

72-Hour Deterioration: 48% at ELEVATED tier, 100% specificity. No HIGH-acuity patient misrouted.

Camera physiological pilot – 25 scenarios: 92% clearance accuracy, 100% UNFIT sensitivity, 96% discrepancy detection.

Three Gaps Found and Corrected

A validation that finds nothing is not a validation. Before finalizing results, the team identified and corrected three gaps. The most common poly-drug intake response “multiple substances” was not triggering CIWA-Ar or COWS scoring; expanding the keyword matching raised withdrawal sensitivity from 75% to 100%. Benzodiazepine withdrawal was receiving no clinical flag because no validated scale exists for it; a seizure-precaution rule was built and added. A calibration error on one passive suicidal ideation case was identified and corrected against published C-SSRS thresholds, which moved suicide sensitivity from 67% to 100%. All three corrections are documented in full and available on request.

Commercial Context

Every correctional facility booking carries documented liability exposure when no structured clinical assessment is conducted. Suicide in custody is the leading cause of jail death in the United States. Withdrawal deaths are preventable and have been litigated. Post-release overdose mortality carries a societal cost that exceeds one million dollars per incident and is documented in the New England Journal of Medicine. These are not hypothetical risks and they show up on legal invoices.

This validation ties sensitivity and specificity data to published clinical standards across every one of those risk categories. A procurement officer, health director, or risk counsel reviewing it has a documented evidentiary basis for a deployment decision. The platform needs no hardware, no IT engagement, and no capital budget. Deployment follows upon facility onboarding and agreement execution.

The 60-day live pilot is structured, pre-specified, and ready. Two 30-day phases, parallel operation alongside standard paper intake, then independent operation with full nurse override authority throughout, converting to a 12-month SaaS agreement on meeting five pre-defined performance criteria. The platform is built, validated, and commercially structured. Facility discussions are active and the Company expects to provide an update in the near term.

Dr. Rahul Kushwah, COO of QScreen AI Inc., stated: “This is not a capabilities update. It is a performance report with published references behind every number. We ran the same cohort through a paper-only intake model and the detection gap was material across every clinical category. The facilities we talk to already know they have a liability problem. What this gives them is the clinical evidence to act on it, with no hardware requirement, no IT project, and a 60-day pilot that converts to contracted revenue on meeting pre-specified criteria.”

About QScreen AI Inc.

QScreen AI Inc. (CSE: QAI) (OTC Pink: PMEDF) (FSE: 3QP) is a health technology company building a proprietary artificial intelligence engine with quantum inspired computing and advanced physiological sensing to clinical and occupational health assessments across correctional facilities, addiction medicine rehabilitation, and industrial workforce screening in multiple jurisdictions.

For more information visit www.q-screen.ai.

Contact
Dr. Rahul Kushwah, COO
Rahul.kushwahphd@gmail.com
(647) 889 6916

Caution Regarding Forward-Looking Information:

This news release may contain forward-looking statements and information based on current expectations. The validation used simulated scenarios and synthetic patient data; live performance may differ materially. The platform is a clinical decision support tool requiring confirmation by a licensed healthcare professional before any action is taken. Although such statements are based on management’s reasonable assumptions, there can be no assurance that such assumptions will prove to be correct. We assume no responsibility to update or revise them to reflect new events or circumstances. The Company’s securities have not been registered under the U.S. Securities Act of 1933, as amended (the “U.S. Securities Act”), or applicable state securities laws, and may not be offered or sold to, or for the account or benefit of, persons in the United States or “U.S. Persons”, as such term is defined in Regulations under the U.S. Securities Act, absent registration or an applicable exemption from such registration requirements. This press release shall not constitute an offer to sell or the solicitation of an offer to buy nor shall there be any sale of the securities in the United States or any jurisdiction in which such offer, solicitation or sale would be unlawful.

QScreen AI screening tools provide risk assessment and decision support only. They are not diagnostic medical devices and are not intended to replace professional medical judgment.

THE CANADIAN SECURITIES EXCHANGE HAS NOT REVIEWED NOR DOES IT ACCEPT RESPONSIBILITY FOR THE ADEQUACY OR ACCURACY OF THIS RELEASE.

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/295679

View more news from QScreen AI Inc.

You are receiving this email because you have previously indicated your interest in receiving news for QScreen AI Inc.

If you no longer want to receive messages from us, you can click here to unsubscribe.

Anti-Spam Policy | Privacy Policy

The post QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios first appeared on PressReleaseCC.

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QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

Nine instruments validated against published standards including Columbia C-SSRS, CIWA-Ar, and COWS; paper-only workflow showed materially lower detection rates across the same scenarios; 60-day live pilot structured and ready

Toronto, Ontario–(Newsfile Corp. – May 4, 2026) – QScreen AI Inc. (CSE: QAI) (OTC […]

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QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

Nine instruments validated against published standards including Columbia C-SSRS, CIWA-Ar, and COWS; paper-only workflow showed materially lower detection rates across the same scenarios; 60-day live pilot structured and ready

Toronto, Ontario–(Newsfile Corp. – May 4, 2026) – QScreen AI Inc. (CSE: QAI) (OTC […]

# summaryMap.en
QScreen AI Reports Internal Validation Results for Correctional Intake Platform: 100% Sensitivity on Suicide Risk and Withdrawal Detection Across 65 Calibrated Clinical Scenarios

Nine instruments validated against published standards including Columbia C-SSRS, CIWA-Ar, and COWS; paper-only workflow showed materially lower detection rates across the same scenarios; 60-day live pilot structured and ready

Toronto, Ontario–(Newsfile Corp. – May 4, 2026) – QScreen AI Inc. (CSE: QAI) (OTC […]