How does product management have more complexity while building a healthcare SAAS product? Where is the underlying technology for artificial intelligence and machine learning? Yogesh will go over the procedure and documentation involved in product development when FDA clearance and approval is required before the market launch of a product. He'll also discuss the validation and verification processes required to get the product FDA cleared. Main takeaways: -AI and ML-based SAAS healthcare products -Emphasis on collaborating with your data science team -The process and relevant documentation to get FDA clearance -The importance of QMS in the healthcare SAAS product life-cycle

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8. Yogesh Sharma
TONIGHT’S SPEAKER

9. SaaS /SaMD Healthcare Products In The Age of
AI and Machine Learning
FDA- Emphasis on Validation and Verification
Presented by: Yogesh Sharma
Date : 24th, May 2018

10. There are no
secrets to
success. It is
the result of
preparation,
hard work,
and learning
from failure.
Colin Powell

11. What are SaMD Product?
• Software intended to be used for one or more medical purposes without being a
hardware medical devices.
• It can diagnose conditions, suggest treatments, and inform clinical management.

12. SaMD Basic Functionality
• Gathering specific kinds of information analyzing that data, and delivering it along
with the software as evidence that the software in question has been designed for
safety and effectiveness.
• Basic Medical field which are implementing the SaMD:
• Oncology
• Radiology
• Immunology
• Cardiology
• General Patient Care
• Etc.

13. SaMD – AI Diagnostic Landscape
Currently Artificial Intelligence enabled diagnostic products are focused on application
such as :
1.Coronary Diagnostics - Arterys, Baylabs, Cardiowise, Genetesis
2. Cancer Diagnostics - Flatiron, Enlitic, Freenome, skinvision, Lunit
3. Wearables - Atlas, Lifegraph, Touchkin, Cardiac insight
4. Neuro Diagnostics – Ginger.io, Mindshare Medical, Imagia, NeuroLex
5. General Medical Imagin/ Diagnostics - Amara health, IBM Watson, Merck, Deep
Genomics, Siemens, Philips, GE Healthcare, DeepMind

14. SaMD – AI: Typical Machine Learning
Steps
Steps followed during typical Machine Learning process:
1. Gathering Data
2. Data Analyzing
3. Choosing Model
4. Training
5. Evaluation
6. Parameter tuning
7. Prediction

15. FDA and SaMD
Check for
safety,
effectiveness
and
cybersecurity
1. FDA is interested in the accuracy, reliability
and precision of output o of the product.
2. FDA wants to ensure that while product
development team took into consideration
Quality management system must be
implemented.
3. Product safety and mitigation plans need to
documented for every sprint of the scrum
process.
4. Cybersecurity design mitigation plan and
implementation.

16. Listed few of the FDA Requirements for
SaMD Products:
1. Implementation of Quality Management System (QMS).
1. Verification process ( Testing and Functional Requirement Document)
1. Validation Process (Business Requirement Document)
1. Risk Assessment:
1. Safety of the product
2. Cybersecurity

17. Quality Management System (QMS
Process)

18. Verification and Validation
Process

19. Questions & Answers

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