In this webinar, we will be covering what exactly an SaMDs, or Software as a Medical Device, and go over some examples with Artificial Intelligence. We will also look at Artificial Intelligence and Machine Learning versus the traditional software. Next, we will go into the regulatory framework for these types of software, then explain how EMMA International can help you get your SaMD to market.

1. www.emmainternational.com
AI and ML in Software
as a Medical Device
Govind Yatnalkar &
Madison Wheeler
1

2. Leaders in FDA
Compliance Consulting
EMMA International Consulting Group, Inc. is a global leader in management consulting services. We focus on
quality, regulatory, and compliance services for the Life Sciences industry.
EMMA International is certified as a Women’s Business Enterprise (WBE) through the Women’s Business Enterprise
National Council (WBENC), the nation’s largest third party certifier of businesses owned and operated by women in
the US.
EMMA International is certified to ISO 9001:2015, a globally recognized quality management standard developed by
the International Organization for Standardization (ISO). Achieving this certificate proves our continued
commitment to continuous improvement and delivering high quality expert services to our clients.
We recognize the commitment to supplier diversity that is embraced by corporations and government agencies today,
and we can add diversity to your supply chain.
2

3. Madison Wheeler
Senior Quality Engineer
Madison.wheeler@emmainternational.com
Office: 248-987-4497
Cell: 734-808-1220
Ms. Wheeler is a Quality Engineer with experience in
technical writing, nonconforming product management,
issue evaluations, and implementing corrective and
preventative actions in the pharmaceuticals and medical
device industries. She has experience cross-functionally
between R&D, lean manufacturing operations, and RA
compliance. Ms. Wheeler also has academic and work
experience with human health-risk engineering controls,
physiological biophysics, and clinical research.
Ms. Wheeler holds a Bachelor of Science of Biosystems
Engineering with a concentration in Biomedical
Engineering from Michigan State University.
3
About the Presenters

4. Govind Yatnalkar
Software Engineer
Govind.Yatnalkar@emmainternational.com
Office: 248-987-4497
Mr. Yatnalkar is a software engineer with expertise in Web
Application development, API programming, Cloud
Computing, and Machine Learning. He has worked on full-
stack development using C++, Python, Java, JavaScript,
and PHP with databases such as MySQL, Oracle, and
MongoDB.
Mr. Yatnalkar holds a Master of Science in Computer
Science from Marshall University and a Bachelor of
Engineering in Computer Engineer
4
About the Presenters

5. • What are SaMDs?
• Examples of SaMDs with AI
• AI/ML VS The Traditional Software
• IMDRF and FDA Proposed Framework
• Summary
• Help from EMMA International
• References
Agenda
5

6. • Software As a Medical Device (SaMD)
• SaMD is defined as the software developed for medical platforms that
provides and performs services without using hardware devices.
• The sole product itself fulfils the medical requirements and is not
dependent on hardware for achieving software targets.
What are SaMDs?
6

7. • Medical document management systems.
• Hospital Information Systems.
• Visual analytic applications which compute
analytics based on continuous live data sets.
• An Android/ Apple app which provides
analytics such as heart rate statistical data
from data server.
• Software that sends images to smartphone
from an MRI medical device for diagnostic
purposes to Computer-Aided Detection
(CAD) that performs image post-processing
to help detect breast cancer cells.
Current Examples of SaMDs
7
This Photo by Unknown Author is licensed under CC BY-SA-NC

8. What is AI and ML?
• Artificial Intelligence – Softwares that can solve
problems by themselves. Provide the ability for
computers to perform tasks executed by humans.
• Machine Learning – A subset of AI. They keep learning
and evolving from huge data sets to perform human
functions. ML are adaptive algorithms.
• Often applications are “AI-Powered” and “ML-Driven”
8

9. • IDx’s IDx-Dran for the detection of
diabetic retinopathy.
• Imagen’s OsteoDetect for scanning
distal radius fracture.
• DreaMed Advisor Pro to
recommend insulin delivery levels.
• Zebra Medical Vision’s AI algorithm
to produce a coronary artery
calcification score from a patient’s
ECG-gated CT scan.
Applications of SaMDs with AI/ ML
9
This Photo by Unknown Author is licensed under CC BY-SA-NC

10. • Efficiency challenges: Manual VS Prediction based results.
• ML are adaptive i.e. they keep changing their models based on
requirements or algorithms if there is low efficiency. Change in Model
selections: architectural and parametric changes.
• Traditional software includes hard rules or dynamic programming.
ML models evolve themselves to achieve required target.
AI/ ML VS The Traditional Software
10
Traditional Software Model AI/ML Based Models

11. • The Two Major Factors
• Risk Categorization Matrix
• The Total Product Life Cycle
• Validation Rules
• Algorithmic Change Protocols
• Cases of Exception
IMDRF & FDA Proposed Framework
11

12. • SaMD Significance of Information: Identifies the intended use of
information as provided by the SaMD. It is classified as follows:
• Treat or Diagnose
• Drive Clinical Management
• Informing Clinical Management.
• State of Healthcare Condition: Identifies the intended user, disease,
and the sample set or population of SaMD. Classified as:
• Critical healthcare condition
• Serious healthcare condition
• Non-Serious healthcare condition
The Two Major Factors
12

13. • The risk matrix has been proposed based on the State of the
Healthcare and Significance of Information:
SaMDs Risk and Testing Categorization Matrix
13
State of
Healthcare
Significance of Information
Treat or Diagnose Drive Clinical
Management
Inform Clinical
Management
Critical IV (Highest) III II
Serious III II I
Non-serious II I I (Lowest)

14. Based on the Pre-Cert program and using TPLC, the following
module has been proposed:
The Total Product Life Cycle (TPLC)
14
Data Selection
/Management
Model Training,
Fine-Tuning
Validation
(Clinical Association,
Analytical Validation,
Clinical Evaluation)
Model in Use/
Live/ Deployed
New Data (User/
Live Data)
Monitor Model
(Performance
Evaluation/ Tracking)
2. Premarket Assurance
of Safety, Performance
or Effectiveness
3. SaMD Pre-specifications
Review/ Algorithmic
Change Protocols
1. Establish clear
expectations/
requirements
4. Real-World
Performance Monitoring

15. • Being an adaptive model, in which changes occur frequently, the
proposed framework is flexible in certain cases and states that it
is not essential to submit a pre-market notification/ application
for the following change cases:
• Performance Changes – Include Clinical and Analytical Performance
Changes
• Input Changes – Parameters fed to algorithm for inputs/outputs.
• Intended-Use Changes.
• Include all anticipated changes in SPS.
Cases of Exception
15

16. The excellence principals of culture of
quality and organizational excellence for
SaMD is carried out by the
Clinical Validation:
• Clinical Association: Check if there is a valid
association between SaMD’s output and target clinical
condition.
• Analytical Validation: Check if SaMD processes input
data to generate accurate, precise, and reliable output.
• Clinical Validation: Check if computed SaMD outputs
achieve the intended purpose in the target population or
sample set from a clinical care perspective.
1. Maintain Quality by Clinical Evaluations
16

17. • The initial pre-market plan comprises
of two major elements: the SPS and
the ACP for assuring safety and
effectiveness:
• Software Pre-specifications (SPS):
Changes given by manufacturer when
the SaMD is “live” or is currently in
use. This results in drafting “Region
of Potential Changes”
• Algorithmic Change Protocol (ACP):
A precise drafted plan which covers
modifications that target the SaMD
goals maintaining the safety and
effectiveness
2. The Pre-Market Assurance Definitions
17

18. • The algorithmic protocols are implemented to cover the
changes while submitting a pre-marketing application such as
501k. the algorithmic change protocols are proposed as follows:
• Data Management which includes collective protocols
and quality assurance while training and testing new
input data.
• Re-training Procedures for reaching objectives and
when there are changes in ML methods including
architecture and parametric changes.
• Performance Evaluations while generating
assessment metrics, statistical analysis plans, and
performance targets.
• Update Procedures during software verification and
validation.
Algorithmic Change Protocols
18
This Photo by Unknown Author is licensed under CC BY-NC

19. • Step 3: Methodology for acquiring changes after initial review
with established SPS and ACP.
• Submit 510k for premarket review OR
• Document changes and analysis in Risk Management and 510(k) files.
• Step 4: Real world Performance Monitoring of SaMD based on
AI/ML:
• Performance Data collection – Gathering data in real-world which may
assist manufacturers to make the device better to improve product
safety, efficacy and usability.
• Transparency – updates from and to users, manufacturers, device
companies, clinicians, patients and as well as the FDA
Framework Step 3 and Step 4
19

20. • Help with premarket submissions
to the FDA
• Ensure your device is compliant
(Irrespective of the existence of
AI/ML) with both FDA
regulations and international
standards
How EMMA International Can Help!
20

21. |21

22. References
• Jack Carfagno (July 2019) 5 FDA Approved Uses of AI in Healthcare. Retrieved on
08/17/2020 from https://medicalfuturist.com/fda-approved-ai-based-algorithms/
• The Medica Futurist. FDA-approved A.I.-based Algorithms. Retrieved on
08/17/2020 from https://medicalfuturist.com/fda-approved-ai-based-algorithms/
• Nick Tippman (July 2019). Regulating Aritificial Intelligence and Machine Learning
Based Software as a Medical Device. Retrieved on 08/17/2020 from
https://www.greenlight.guru/blog/regulating-artificial-intelligence-machine-
learning-software-as-a-medical-device
• Cohen Healthcare. Proposed Regulatory Framework for Modifications to Artificial
Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device
(SaMD) – Part Two. Retrieved on 08/17/2020 from
https://cohenhealthcarelaw.com/2020/07/proposed-regulatory-framework-for-
modifications-to-artificial-intelligence-machine-learning-ai-ml-based-software-as-
a-medical-device-samd-part-two/
• Johner Institute. Software as Medical Device: Definitions and Classification Aids.
Retrieved on 08/17/2020 from https://www.johner-
institute.com/articles/software-iec-62304/software-as-medical-device/.
• Dave Muoio (November 2018). Roundup: 12 healthcare algorithms cleared by the
FDA. Retrieved on 08/17/2020 from
https://www.mobihealthnews.com/content/roundup-12-healthcare-algorithms-
cleared-fda
22