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        • R-002-007 Process validation card 2023_001
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        • R-002-007 Process validation card 2023_003
        • R-002-007 Process validation card 2023_004
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  • R-002-007 Process validation card 2023_012

R-002-007 Process validation card 2023_012

Version control​

Reason for reviewDateVersion idChange
Initial creation202303081N/A
Management Review 2024202404152Annual revalidation
Management Review 2025202504153Annual revalidation
Update202602234Added risk analysis section

Process​

Design and development

Requirements​

To find the best technology to develop an innovative and effective skin-condition medical device that can improve the accuracy and consistency of diagnoses, expand access to specialized medical care, and ultimately improve patient outcomes.

Selection description​

Deep learning is particularly well-suited for developing a medical device that diagnoses skin diseases because it can be trained on large datasets of medical images. Skin diseases are often diagnosed by examining images of the affected area, and deep learning algorithms can be trained to recognize patterns and features that are associated with specific skin conditions.

In addition, deep learning can help to overcome some of the limitations of traditional diagnostic methods for skin diseases. For example, visual diagnosis of skin diseases can be subjective and may vary depending on the experience and skill of the clinician. Deep learning algorithms, on the other hand, can be trained on a large and diverse set of images, which can help to reduce the risk of misdiagnosis and increase the accuracy and consistency of diagnoses.

Moreover, deep learning can also be used to develop a medical device that is capable of diagnosing a range of skin diseases with a high level of accuracy. This can be particularly important in areas where access to dermatologists or specialized medical care is limited. By using deep learning, we can develop a medical device that is both reliable and accessible, enabling more patients to receive accurate diagnoses and appropriate treatment.

Validation​

We have demonstrated since we founded the company that we can improve the quality of patient care while reducing costs and increasing efficiency manufacturing a medical device to help practitioners on the diagnosis and follow up of different skin-conditions.

Identified risks​

RiskPotential impactControl measureStatus
Model performance degradation over timeReduced diagnostic accuracyContinuous monitoring, periodic retraining, post-market surveillance (GP-007)Controlled
Training data biasInaccurate results for underrepresented populationsDiverse dataset collection, clinical validation studiesMonitoring

Record signature meaning​

  • Author: JD-004
  • Review and approval: JD-001

Signature meaning

The signatures for the approval process of this document can be found in the verified commits at the repository for the QMS. As a reference, the team members who are expected to participate in this document and their roles in the approval process, as defined in Annex I Responsibility Matrix of the GP-001, are:

  • Author: Team members involved
  • Reviewer: JD-003 Design & Development Manager, JD-004 Quality Manager & PRRC
  • Approver: JD-001 General Manager
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  • Version control
  • Process
  • Requirements
  • Selection description
  • Validation
  • Identified risks
  • Record signature meaning
All the information contained in this QMS is confidential. The recipient agrees not to transmit or reproduce the information, neither by himself nor by third parties, through whichever means, without obtaining the prior written permission of Legit.Health (AI Labs Group S.L.)