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Our Services

With our innovative data services we provide you with the regulatory data required for the development, approval and post-market surveillance of your medical products.

Clinical Workflow Analysis

Integrating Clinical Data Centers

Clinical Workflow Analysis

Expert Evaluation of the Common Clinical Workflow (Best Practice)

Clinical Use Case Analysis

Integrating Clinical Data Centers

Clinical Workflow Analysis

Testing of the Planned Clinical Use Case to Optimize the Standard of Care

Integrating Clinical Data Centers

Integrating Clinical Data Centers

Integrating Clinical Data Centers

Enabling Data Access through

Framework Contracts 

Acquisition of Clinical Raw Data

Integrating Clinical Data Centers

Integrating Clinical Data Centers

Ensuring Fast and Reliable Data Exchange


Creating Annotation Guidelines

Creating Annotation Guidelines

Creating Annotation Guidelines

Standardizing Clinical Training Data


Annotating Clinical Data

Creating Annotation Guidelines

Creating Annotation Guidelines

Ensuring “Gold Standard” Annotation with Clinical Experts

Data Quality Management

Creating Annotation Guidelines

Data Quality Management

Providing Highest Data Quality through Controlled Processes

Validation & Approval

Creating Annotation Guidelines

Data Quality Management

Ensuring Regulatory Approval & Clinical Evidence by Clinical Performance Testing

Clinical Workflow Analysis

  • Customer Problem
    Inefficiencies and inconsistencies in the clinical workflow can hinder the integration of AI-based medical devices.


  • Approach
    Our experts analyze the existing clinical workflow, identify weaknesses, and provide recommendations for optimization.


  • Result
    A detailed report with best practices and optimized workflow processes that support the implementation of the AI medical device.

  • Current Workflow Analysis
    Our experts document and analyze the entire clinical workflow, from diagnosis to treatment and follow-up care.


  • Identification of Problems and Bottlenecks
    Through interviews and observations, specific challenges and inefficiencies in each sector of the clinical workflow are identified.


  • Development of Optimization Proposals
    Based on the findings, tailored solutions are developed to address existing issues and facilitate the integration of the AI product.


  • Implementation and Training
    The proposed changes are implemented, and clinical staff are trained to ensure a smooth transition.


  • Continuous Monitoring and Adjustment
    The new workflow is continuously monitored and adjusted as needed to ensure optimal long-term outcomes.

Clinical Use Case Analysis

  • Customer Problem
    Unclear or inefficient clinical use cases can impact the acceptance and effectiveness of the AI medical device.


  • Approach
    Conducting tests and simulations of the planned clinical use case to assess and optimize its effectiveness and efficiency.


  • Result
    An optimized clinical use case that enhances the standard of care and supports the implementation of the AI medical device.

  • Identification of Potential Use Cases
    In collaboration with clinical experts, potential application scenarios for the AI product are identified. This includes various diseases, patient groups, and treatment situations.


  • Interviews and Workshops with Clinics
    Through discussions and workshops with experienced clinicians, the identified use cases are analyzed in detail. This includes assessing how, when, and by whom the AI product should be used.


  • Evaluation of Clinical Requirements
    The requirements and expectations of clinicians are gathered and systematically analyzed. This helps to understand specific needs and challenges.


  • Development and Validation of the Use Case
    Based on the collected data, a detailed use case is developed and validated with clinicians to ensure it is practical and realistic.


  • Creation of a Use Case Report
    At the end of the process, a comprehensive report is generated, summarizing the defined use cases, clinical requirements, and recommendations for product development.

Integrating Clinical Data Centers

  • Customer Problem
    Challenges in accessing necessary clinical data for the development and validation of AI medical devices.


  • Approach
    Establishing framework agreements with data centers to ensure seamless data access.


  • Result
    Secured access to clinical data sources essential for the development and validation of the AI medical device.

  • Identification of Potential Data Centers
    The first step involves identifying a network of potential data centers. This can initially be done within the existing network and expanded if needed.


  • Screening and Contacting
    Potential data centers are contacted to assess their willingness and interest. Basic information about available data, its quality, and existing processes is collected.


  • Qualification of Data Centers
    Qualification criteria include the required number of cases (retrospective or prospective), existing processes for conducting such studies, and prior experience. Centers that do not meet these requirements are excluded.


  • Negotiation of Compensation
    Compensation for the clinics is negotiated, which may vary by location. This ensures that clinics are fairly compensated for their contributions.


  • Establishment of Framework Agreements
    Framework agreements are concluded with qualified data centers to define the terms of collaboration and data access.

Acquisition of Clinical Raw Data

  • Customer Problem
    Delays and uncertainties in the exchange of clinical raw data can extend development time.


  • Approach
    Establishing processes and technologies that enable fast and reliable data exchange.


  • Result
    A comprehensive, secured set of clinical raw data that can be used for the training and validation phases of the AI medical device.

  • Screening and Collection of Datasets
    Clinics must either screen retrospectively available datasets or prospectively collect new data. This process requires precise planning and coordination.


  • Development of Study Protocols
    Similar to clinical studies, detailed study protocols must be created to describe the entire data collection process. These protocols must comply with all relevant ethical and regulatory requirements.


  • Ethics Committee Approval
    Before data collection can begin, the study protocols must be approved by an ethics committee. This ensures that data collection is ethically sound and legally compliant.


  • Data Aggregation and Transfer
    Once approved, the data is collected, aggregated, and prepared for transfer according to the protocols. All steps must be documented and monitored to ensure data quality and integrity.


  • Data Anonymization
    Before transferring the data, it must be anonymized to protect personal information. This means removing all identifiable details to safeguard patient privacy.


  • Support and Monitoring
    To ensure a smooth and efficient process, the experienced team at Inventor AI provides comprehensive support and monitoring. We oversee the entire process to ensure all steps are conducted correctly and in compliance with regulations.

Creating Annotation Guidelines

  • Customer Problem
    Inconsistent and non-standardized annotations from different clinical experts can compromise the quality of training data.


  • Approach
    Developing clear and standardized annotation guidelines for clinical training data.


  • Result
    Detailed annotation guidelines that ensure consistent and high-quality data annotation across different annotation teams and clinical experts.

Annotating Clinical Data

  • Customer Problem
    A lack of high-quality annotated data can impact the accuracy and reliability of the AI model.


  • Approach
    Conducting data annotation by clinical experts following the established annotation guidelines.


  • Result
    High-quality annotated clinical data that meets the “gold standard” and serves as training data for the AI medical device.

  • Definition of Annotation Requirements
    The first step is to define the specific requirements for annotations. These may include simple categorical labels or complex pixel-based segmentations, depending on the type of medical data and the AI product’s application.


  • Training of Annotators
    Clinical experts responsible for annotations receive comprehensive training to ensure they understand and adhere to the specific requirements and standards.


  • Annotation Creation
    Data is annotated according to the defined requirements. This can be done manually or through semi-automated processes supported by specialized software tools.


  • Quality Management and Control
    A robust quality management system (QMS) is essential to ensure annotation quality. This includes regular reviews and validations by experienced clinicians and experts.


  • Error Monitoring and Corrective Actions
    The complexity of QMS processes depends on the error susceptibility of annotations and the potential risk to the medical device’s performance. Continuous monitoring and immediate corrective actions help minimize the error rate.


  • Risk Assessment and QMS Adaptation
    QMS processes are continuously reviewed and adjusted to meet specific requirements and risks. This ensures that annotations meet the highest quality standards while minimizing risks for both patients and product performance.

Data Quality Management

  • Customer Problem
    Poor data quality and data leaks can lead to errors and inaccuracies in the AI medical device.


  • Approach
    Implementing controlled processes to ensure and monitor data quality as well as prevent data leaks throughout the entire development process.


  • Result
    A detailed quality management report that guarantees the highest standards of data quality for the AI medical device.

  • Data Acquisition and Preparation
    The first step in data quality management (DQM) is acquiring and preparing the data. This includes collecting, cleaning, and integrating data from various sources to create a unified and high-quality data foundation.


  • Quality Control and Validation
    Comprehensive quality control and validation processes are conducted to ensure that the data is error-free and of high quality. This includes checking for completeness, accuracy, consistency, and relevance.


  • Data Enrichment
    Enhancing the data with additional information and context increases its value. This can be achieved by integrating external data sources or applying analytical tools.


  • Implementation of QMS Processes
    A robust quality management system (QMS) is essential for DQM. This involves defining quality standards, implementing monitoring and control mechanisms, and continuously improving processes.


  • Error Monitoring and Corrective Actions
    Continuous monitoring and immediate corrective actions are crucial to maintaining high data quality. This includes identifying and fixing data errors as well as implementing preventive measures.


  • Documentation and Reporting
    Comprehensive documentation and regular reporting are essential to demonstrate data quality and ensure regulatory compliance. This includes detailed reports on quality control measures, validation processes, and corrective actions taken.

Validation & Approval

  • Customer Problem
    Challenges in meeting regulatory requirements and demonstrating clinical evidence for the AI medical device.


  • Approach
    Conducting clinical performance tests and preparing the necessary documentation to ensure regulatory compliance.


  • Result
    A comprehensive validation plan and approval report that ensures regulatory conformity and provides clinical evidence for the AI medical device.

  • Planning of Validation Studies
    Support with planning of the validation study including determination of statistical endpoints, that prove the clinical evidence best.


  • Curation of the Test Dataset
    The next step is the careful selection and curation of the test dataset. It must be representative of the entire patient population that the AI product will later serve. Diversity in data is essential to cover various clinical practice scenarios.


  • Recruitment of Experts
    Independent and experienced experts are recruited to create the “Ground Truth Dataset.” These experts ensure that validation is based on solid, clinically relevant data.


  • Conducting Validation Studies
    Validation studies are performed under controlled conditions to assess the AI model’s performance, accuracy, and reliability. This can take place in both a stand-alone setting and real clinical environments.


  • Data Analysis and Documentation
    The data collected during validation is thoroughly analyzed and documented. The results must be detailed and traceable to demonstrate the clinical performance of the AI model.


  • Clinical Evaluation Report
    Based on the validation study results, a clinical evaluation report is created. This document is essential for regulatory approval and includes all relevant data and evidence regarding the AI model’s performance and safety.

Contact

Inventor AI

c/o M3i GmbH, Pettenkoferstraße 24, 80336 München, Germany

info@inventorai.de

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