AI Startup Consultation
Strategic consultation designed for cutting-edge AI startups in the medical space. We provide comprehensive guidance spanning strategy, actionable execution, compliance, and cybersecurity to accelerate market entry safely.
Strategy & Planning
Product Screening
Product / Solution Qualification activities.
Learn moreSegment Identification
Identification of specific addressable product segments.
Learn moreKOL Identification
Identification of Key Opinion Leaders (KOLs) for your product.
Learn moreArchitecture Qualification
Architecture / Deployment model qualification and necessary change proposals.
Learn morePredicate Analysis
Competitor analysis based on FDA filings and recent FDA clearances in this ROI.
Learn moreExecutive Consultants
Identification of key former executives for potential strategic consultation.
Learn moreExecution & Meetings
Exploratory Calls
Discussions with KOLs, executive consultants, and potential partners.
Learn moreIntroductory Partner Calls
Formal introductions connecting you with strategic potential partners.
Learn moreIn-Person Meetings
Potential in-person meetings at partners' campuses or decided sites for detailed business discussions.
Learn moreProduct Demonstrations
Coordination and structuring of targeted product demos.
Learn moreDeck Preparation
Custom business presentation deck review and preparation help tailored for specific partners.
Learn moreMeeting Management
Detailed MoM preparation with next steps. Leading activities of meeting actions via email/channels.
Learn moreOperations & Compliance
Action Tracking
Track and lead closure of action items and technical help needed by partners.
Learn moreContractual Agreements
Track and ensure the closure of NDAs and other vital agreements.
Learn moreSLA Preparation
Expert assistance in Service Level Agreement (SLA) preparation.
Learn moreCybersecurity & Data Privacy
Comprehensive consultation including "secure by design" principles, compiling Software Bill of Materials (SBOM) for all third-party components, and continuous vulnerability monitoring tailored to healthcare regulatory constraints.
Learn moreGood Machine Learning Practice (GMLP) Alignment
Our consultation is anchored in the IMDRF's Good Machine Learning Practice for Medical Device Development: Guiding Principles (IMDRF/AIML WG/N88 FINAL: 2025). We help AI startups embed these 10 internationally recognized principles across the total product life cycle — from intended use definition to post-market monitoring — to accelerate safe, effective, and high-quality AI-enabled medical devices.
Intended Use & Multi-Disciplinary Expertise
Establish a deep understanding of the device's intended use and context within the clinical workflow, leveraging multi-disciplinary expertise across the total product life cycle to address clinically meaningful needs.
Learn moreSoftware Engineering & Security Practices
Implement robust software engineering, data quality assurance, cybersecurity, risk management, and quality management practices throughout the device life cycle.
Learn moreRepresentative Clinical Datasets
Ensure datasets used for training, testing, and monitoring are representative of the intended patient population — across age, sex, race, ethnicity, geography, and medical condition — to manage bias and dataset drift.
Learn moreTraining & Test Set Independence
Maintain appropriate independence between training and test datasets, addressing all potential sources of dependence including patients, sites, and data acquisition methods.
Learn moreFit-for-Purpose Reference Standards
Select reference standards informed by broad consensus and appropriate expertise, with documented rationale aligned to the device's intended use environment.
Learn moreModel Choice Tailored to Data & Intended Use
Evaluate model design suitability against available data and intended use, actively mitigating known risks such as overfitting, performance degradation, and security threats.
Learn moreHuman-AI Interaction Assessment
Assess the device in the context of the intended clinical workflow, considering human factors such as user expertise, interpretation of model outputs, potential for overreliance, and reasonably foreseeable misuse.
Learn morePerformance Testing in Clinical Conditions
Execute methodologically and statistically sound test plans that generate clinically relevant performance information, independent of the training dataset and across relevant subgroups.
Learn moreClear, Essential User Information
Provide users with contextually relevant information including intended use, benefits, risks, subgroup performance, study methodology, acceptable inputs, known limitations, and the basis for model output.
Learn morePost-Deployment Monitoring & Re-training Controls
Maintain ongoing real-world performance monitoring with risk-based controls to manage overfitting, unintended bias, and model degradation when re-training deployed models.
Learn moreA Note on Generative AI
As generative AI becomes more prevalent in healthcare technologies, GMLP becomes even more critical. Foundation models that are not under the provenance of the medical device manufacturer can introduce unique risks, and demonstrating device performance becomes more challenging. Qscription helps startups navigate these emerging considerations alongside fundamental software engineering practices.
Source: IMDRF/AIML WG/N88 FINAL: 2025 — Good Machine Learning Practice for Medical Device Development: Guiding Principles, Artificial Intelligence/Machine Learning-enabled Working Group, International Medical Device Regulators Forum (27 January 2025).