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        • R-002-007 Process validation card 2023_001
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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 id
First version202303081
Written byReviewed byApproved by

E-Signature (Appfire integration):

Signature logo

María Diez

6E206A2F15299C7C6E416D11903F365C

Signer name: María Diez

Signing time: Sun, 05 Mar 2023 08:34:18 GMT

Reason: Creation of document

E-Signature (Appfire integration):

Signature logo

Alfonso Medela

DA66021199574C5989D8B451B63DC242

Signer name: Alfonso Medela

Signing time: Tue, 07 Mar 2023 08:46:40 GMT

Reason: Reviewed

E-Signature (Appfire integration):

Signature logo

Andy Aguilar

694DA2B1C7AC8395560124C183EB13EE

Signer name: Andy Aguilar

Signing time: Wed, 08 Mar 2023 08:45:48 GMT

Reason: Approving document

Quality manager (QM)Technical manager (PRRC)General manager (GM)

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.

Process approval​

Approved by

E-Signature (Appfire integration):

Signature logo

Andy Aguilar

1439E09324D18FDF043C28BC3A6F779B

Signer name: Andy Aguilar

Signing time: Wed, 08 Mar 2023 13:24:41 GMT

Reason: Process approval

JD-001 General Manager (GM)
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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.)