MDS – G010

Guidance on Artificial Intelligence (AI)

and Machine Learning (ML) technologies based Medical Devices

Version Number: 1.0

Version Date: 29/11/2022

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Table of Content
Introduction ......................................................................................................................... 3

Purpose ______________________________________________________________3

Scope ________________________________________________________________3

Background ___________________________________________________________3

Medical Device Item Classification and Criteria ................................................................ 4

Medical Device Classification Criteria ______________________________________4

Intended use___________________________________________________________6

Clinical Evaluation.............................................................................................................. 6

Risk Management ............................................................................................................. 13

Quality Management Systems .......................................................................................... 16

Change Notification .......................................................................................................... 18

Annexes............................................................................................................................. 20

Annex (1): Definitions & Abbreviations ____________________________________21

Further Relevant Reading Materials ................................................................................. 22

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Introduction

Purpose

The purpose of this guidance is to clarify the requirements for obtaining Medical Devices
Marketing Authorization (MDMA) for Artificial Intelligence (AI) and Machine Learning
(ML) based medical devices, in order to place them on the market within KSA.

Scope

This guidance applies to Artificial Intelligence (AI) and Machine Learning (ML)
technologies that diagnose, manage or predict diseases by analyzing medical data.

Background

SFDA has issued this guidance document in reference to the following:

- Article 8 stipulating that “medical devices cannot be marketed/used unless
obtaining a registration and marketing Authorization, and The SFDA may exempt
some medical devices from the requirement to obtain a marketing Authorization,
after ensuring their safety, and not using them for commercial purposes”.
- Requirements specified in “Requirements for Medical Device Marketing
Authorization (MDS– REQ 1)”.
- Guidance to Pre-Market Cybersecurity of Medical Devices MDS-G38
- Guidance to Post-Market Cybersecurity of Medical Devices MDS-G37

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Medical Device Item Classification and Criteria
Medical Device Classification Criteria

A. Overview

Development of Artificial Intelligence (AI) and Machine Learning (ML) medical devices
is continuously evolving and rapidly improving at a rapid pace. Diverse and more complex
functions are coming in line with the purpose of improving patients care. This section aims
to present the Medical device classification criteria and control methods for these emerging
medical devices.
The intended use of Artificial Intelligence (AI) and Machine Learning (ML) technologies
will determine whether they will be regulated as a medical device. The intended use is
based on the product specifications and instructions of use along with any information
provided by the product developer.

If the Artificial Intelligence (AI) and Machine Learning (ML) devices are intended by the
Product developer to be used for investigation, detection diagnosis, monitoring, treatment,
or management of any medical condition, disease, anatomy or physiological process, it will
be classified as a medical device subject to SFDA’s regulatory controls.

Examples of Artificial Intelligence (AI) and Machine Learning (ML) technologies that
are classified as medical device:

 In-vitro diagnostic tools. Artificial Intelligence (AI) and Machine Learning (ML)
technology that has the ability to recognize different types of cells, quantify, and
analyze the results.
 AI-based biosensors that predict tendencies and probability of disease, the device
may provide information of dangerous vital signals and give recommendations for
health improvement.

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B. Regulatory approach to Artificial Intelligence (AI) and Machine Learning (ML)
medical devices

Premarket Review Considerations

The manufacturer (developer) of Artificial Intelligence (AI) and Machine Learning (ML)
based medical devices or in vitro diagnostics is expected to meet the technical
documentation required for Medical Devices Marketing Authorization that is specified
within MDS-REQ 1, Annex (3) Medical Device Technical Documentation, or Annex (4)
IVD Technical Documentation, which list them as follow:

1) Device Description and Specification, Including Variants and Accessories.

2) Information to be provided by the Manufacturer.

3) Design and Manufacturing Information.

4) Essential Principles of Safety and Performance.

5) Benefit-Risk Analysis and Risk Management.

6) Product Verification and Validation.

7) Post Market Surveillance Plan.

8) Periodic Safety Update Report and Post Market Surveillance Report

Special consideration:

When a manufacturer is conducting verification and validation testing, the nature and
extent of the validation depends upon the risks associated with the device, the intended
purpose, the anticipated use of the device in the digital health system, and the intended use
of the device, Documentation which demonstrates the following performance testing
should be included in the submission:

 Verification that the device meets its design specifications;

 Validation that the device performs as intended;

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  Usability study that verify that the information provided to the user to connect to
the device and to allow the user to ensure that the connection has been made
correctly; and
 Validation that the device will perform safely and within specification when used
under normal conditions and abnormal conditions that are reasonably likely to
occur (e.g. receives data outside of specification, connected to an unintended device
or system).

Intended use

Medical devices are classified based on their intended use and degree of potential risk to
human body upon use in accordance with the Medical Devices Marketing Authorization
requirements that are specified within MDS-REQ 1, Annex (5) Risk Classification Rules
for Medical Devices”.
In accordance with Article (1) of the Medical Devices Law, a medical device means Any
instrument, apparatus, implant, in vitro reagent or calibrator, software, or material used for
operating medical devices, or any other similar or related articles, intended to be used alone
or in combination with other devices for diagnosis, prevention, monitoring, controlling,
treatment, or alleviation of disease or injury, or for compensation for an injury; investigation,
replacement, modification, or support of the anatomy or of a physiological process; supporting
or sustaining life; controlling or assisting conception; sterilization of medical devices and
supplies; providing information for medical or personal purposes by means of in vitro
examination of specimens derived from the human body; and does not achieve its primary
intended action by pharmacological, immunological or metabolic means, but which may be
assisted in its intended function by such means.

Clinical Evaluation

There is no internationally aligned framework for the clinical evaluation of AI/ML-based

medical devices. A manufacturer of AI/ML-based medical devices is expected to provide
clinical evidence of the device’s safety, effectiveness and performance before it can be
placed on the market.

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According to IMDRF description of the process of clinical evaluation explained in the
SaMD: Clinical Evaluation guidance, the manufacturer needs to generate evidence to
demonstrate a valid clinical association, analytical/technical validation, and clinical
validation of AI/ML-based medical device. This clinical evaluation pathway emphasis that
this process should be an iterative and continuous as part of the quality management system
for AI/ML-based medical devices. The requirements for clinical evaluation apply to all risk
categories of AI/ML-based medical devices.

To demonstrate a valid clinical association between the output of AI/ML-based medical

device and the targeted clinical condition, the manufacturer need to provide evidence that
the device output is clinically accepted based on existing evidence in published scientific
literature, original clinical research, and/or clinical guidelines. The manufacturer should
demonstrate the relevance of available data to the clinical problem and current clinical
practice, and that it aligns with the AI/ML-based medical device’s intended use. If the
manufacturer cannot confirm the scientific validity of the device based on an established
body of evidence, new evidence needs to be generated, for example, through conducting
secondary data analysis or a clinical trial. Since the evidence underlying the clinical
association validity of AI/ML-based medical devices are immature, and due to the low
confidence in this evidence as applied to AI/ML-based medical devices, AI/ML-based
medical devices often will be classified as having novel clinical association as these devices
may involve new inputs or outputs, novel algorithms, new intended target population, or
new intended use.

Next, the manufacturer of AI/ML-based medical devices should demonstrate the expected
analytical/technical validation. The analytical validation evaluates the correctness of input
data processing by the AI/ML-based medical devices to create reliable output data. The
manufacturer should provide objective evidence that the device specified requirements
have been fulfilled, and demonstrate that the device meets its specifications for a specific
intended use. This evidence is generally generated during the verification and validation
activities as part of the quality management system; usually using labeled reference
datasets.

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Lastly, AI/ML-based medical devices’ manufacturers are expected to demonstrate clinical
validation. Clinical validation is a necessary component of clinical evaluation for all
AI/ML-based medical devices and it measures the ability of AI/ML-based medical device
to yield a clinically meaningful outcome associated to the intended use of the device output
in the target population in the context of clinical care. Clinical validation may only be
conducted upon successful completion of analytical/technical validation. Clinical validity
is evaluated during the development of the AI/ML-based medical device before it is placed
on the market (pre-market) and after placement on the market (post-market). The
manufacturer can demonstrate the clinical validity by referencing existing data from studies
conducted for the same intended use, or if available data references studies conducted for
a different intended use, extrapolation of such data can be justified, otherwise, the
manufacturer will be required to generate new clinical data for the intended use.

The clinical validation should list the data sources that have been evaluated and that both
support and contradict the manufacturer claim that the benefits have been achieved. The
types of data necessary to assure safety and effectiveness during the clinical validation,
including study design, will depend on the function of the AI/ML-based medical device,
the intended use, and the risk it poses to users. Example of metrics of clinical validation in
the intended use environment with the intended user include, but not limited to: specificity,
sensitivity, positive predictive value (PPV), negative predictive value (NPV), likelihood
ratio negative (LR-), likelihood ratio positive (LR+), and clinical usability. The likelihood
ratio of a positive result should be as large as possible whereas the likelihood ratio of a
negative result should be as small as possible. All metrics, except the likelihoods, are
evaluated in the range of 0-1 or in percentage from 0 to 100%:

This is a device dependent example, not a rule.

Evaluation
<0.6 – unsuitable
0.61 - 0.8 – revision required
> 0.81 – admissible for clinical validation

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Certain AI/ML-based medical devices may require independent review of the results of the
clinical evaluation to ensure that the AI/ML-based medical device is clinically meaningful
to users. In this case, the clinical evaluation of the AI/ML-based medical device should,
where possible or as far as possible, be reviewed by someone who has not been
significantly involved in the development of the AI/ML-based medical device, and who
does not have anything to gain from the device, and who can objectively assess the device’s
intended purpose and the conformity with the overall clinical evaluation evidence. The
level of evaluation and independent review should be proportionate with the risk posed by
the AI/ML-based medical device.

If the clinical evaluation is based on a comparator device, the manufacturer must

demonstrate sufficient clinical and technical equivalence of the other device, including
explicit evaluation of the AI/ML algorithm/model. Manufacturers that cannot demonstrate
equivalence must have sufficient evidence or conduct clinical trials to establish and verify
clinical safety and effectiveness.

Since there are no international standards for the clinical evaluation of AI/ML-based
medical devices, the minimum standards and good practice for clinically evaluating
AI/ML-based medical devices as partially adapted from WHO:

 The manufacturer should assess whether the promised medical benefit is achieved is
consistent with the state of the art.

 The manufacturer should list alternative methods, technologies and/or procedures and
compares these alternatives with respect to clinical benefits, safety/risks, and
performance.

 The manufacturer should assess whether the promised medical benefit is achieved
with the quality parameters.

 The outcomes assessed should be pre-defined by manufacturers and should be

reported using standard performance metrics for the specific field to facilitate
comparisons across studies.

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  Manufacturers should provide assurance that metrics of effectiveness and safety
include outcomes that are meaningful to patients and clinical outcome, i.e.
measures of improvement in patient outcomes, clinical process or time efficiency,
measures of acceptable unintended consequences, and absence of harm to patients.

 Manufacturers are advised to evaluate user and system elements, this may include
assessments of:

 Acceptability and changes in user’s experience.

 Human-computer interactions, including how the output is interpreted and

actioned.

 Human factors surrounding its use, i.e. account for user variability (such as the
learning curve, understanding, trust, and behaviors) and the added biases occurring
as a result.

 Variance in practice settings.

 Wider impact on care pathways.

 Analytical validation should be done using large independent reference dataset

reflecting the intended purpose and the diversity of the intended population and
setting. The reference dataset should meet the following requirements unless there is
sufficient evidence to show that a requirement does not need to be met:

1. The normal-to-abnormal ratio should reflect the prevalence of the target

condition in the population;

2. Several medical centers should source the reference dataset to introduce the data
heterogeneity;

3. Demographic, socio-economic characteristics and basic health indicators in the

reference dataset should correspond to the population’s average characteristics
in the target region;

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 4. The proposed size of the reference dataset should be justified per statistical
considerations, and the desired diagnostic accuracy by the standard metrics
indicated above;

5. Reference datasets used in clinical tests for registering the device as a medical
device should not be publicly available (to exclude the possibility of training AI
algorithms on reference datasets).

 The manufacturer should generate evidence on device performance that can be

generalized to the entire intended population, demonstrating that performance will not
deteriorate across populations and sites:

 Conduct multisite clinical investigation that account for variations on different

sites and allow identification of unintended bias and reliability.

 Analyze the performance of the model for appropriate subgroups, i.e.

demographics, geographic location, disease subtype, etc. The statistical
distribution of data must correspond to the real environment.

 Demonstrate adequacy of the sample size and power calculation.

 Consider the effects of confounding factors.

 The manufacturer of AI/ML-based medical devices should test performance by

comparing it to gold standard, i.e. the reference standard that is being used to evaluate
the model has to be evidence-based, demonstrating that the results are repeatable and
reproducible in different settings.

 The effects of AI/ML-based medical devices should be evaluated in clinically relevant

conditions, i.e. this requires integration into the existing clinical workflow with a
platform to collect, store, and process data, and to deliver the outputs to users in a
timely manner. This will provide assurance that AI/ML-based medical devices are
safe, effective and performant – not just under test conditions but in the real world.

 Because AI/ML-based medical devices aim to enhance users’ performance, not to

replace them, evaluation of model performance in efficacy/effectiveness studies
requires comparisons of clinicians’ performance with and without the AI/ML-based

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 medical device; not the performance of clinicians versus AI/ML-based medical device
alone, in order to demonstrate the impact of the device on clinical practice.

 Manufacturers in their study design should consider proactively the effects that their
studies may have on healthcare organizations and potentially explore the possibility
of prospective real-world studies in order to minimize selection bias, have more
control over variables and data collection, and examine multiple outcomes. The
majority of published evidence to date has consisted of early phase retrospective
validation studies which are in fact in silico (i.e. performed by computer, as opposed
to in vivo) assessments of datasets used to test performance accuracy of AI/ML-based
medical devices’ algorithms.

 Report the result of the clinical investigation using AI-specific reporting guidelines
and standards.

 The manufacturer should locally validate the AI/ML-based medical devices that
developed and approved in other jurisdictions.

 AI/ML-based medical device is unique in its ability for continuous learning, hence,
manufacturers are required to use post-market continuous monitoring of safety,
effectiveness, and performance to gather and validate relevant performance
parameters and metrics for the AI/ML-based medical device in real-world setting in
order to understand and modify software based on real-world performance.

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Risk Management

The implementation of new technologies such as Artificial intelligence (AI) and Machine
Learning (ML) may present risks that could jeopardize patient health and safety, increase
inequalities and inefficiencies, undermine trust in healthcare, and adversely impact the
management of healthcare. Thus, in line with SFDA “Requirements for Medical Devices
Marketing Authorization (MDS-REQ 1)” manufacturers are required to demonstrate that
their medical devices do not pose unacceptable risks, and that the benefits of their intended
use outweigh the overall residual risk.

Since Artificial intelligence (AI) and Machine Learning (ML) are software-driven, the
unique or elevated risks are those around data management, feature extraction, algorithm
training, model evaluation, and cyber and information security. Safety risk may be
introduced by Machine learning systems by learning incorrectly, making wrong inferences,
and then recommending or initiating actions that, instead of better outcomes, can lead to
harm. Occasionally, machine learning systems detect correlations in data sets instead of
causations, which can lead to incorrect conclusions.

The fundamental requirements for safety should include results of the evaluations
regarding the limitations and the performance of the ML algorithm that may not produce
100% accuracy and the necessary training of human personnel for an adequate management
of the algorithm errors. Thus, data scientists should be included in the cross-functional
team that perform risk management tasks.

There should be a risk management plan that includes:

 The scope of risk management activities

 Assignment of responsibilities
 Requirements for review of the activities
 Risk acceptability criteria
 Method to evaluate overall residual risk
 Activities of the implementation and effectiveness of the risk control measures
 Activities to collect and review post-production information

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  The criteria used to trigger an update, risk management of the update process itself,
and provisions for returning the product to a previous version if necessary
 For ML-based medical devices that communicate with other devices or IT systems,
the scope of the plan should include risks related interoperability.
 Cyber security risks
Risk analysis should include the following questions about ML medical device and explore
the risks associated with each:

 Does the software provide diagnostic or treatment recommendations?

 If so, how significant is the information in influencing the user?
 What is the target population of the device (e.g., is the patient condition non-
serious, serious, or critical?
 Which is the urgency/emergency of the information provided?
 Does the algorithm provide options and likelihood of appropriateness?
 Are errors detectable?
 What autonomous functions, if any, does the system provide? Is the algorithm
configurable
 Does the ML-enabled medical device have the ability to learn over time?
 Is the device capable of adjusting its performance characteristics over time?
 What are potential off-label uses of the device? What are the potential foreseeable
misuse?
 Are there contra-indications due to restricted patient's conditions in data used to
train, test, and validate the ML?
 Is the system intended to learn over time, and if so, is there any potential impact to
the intended use?

Additional risks that should be taken into consideration include: failure to act (the user does
not have confidence in the ML), data (or use) drift (locked ML that performed well a decade
ago might not perform as well today), abundance of data but a lack of knowledge and
Fragmented data throughout different formats

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 Where the probability of occurrence cannot be estimated (which can often be the
case for ML applications), the risk should be estimated based on the severity of
possible harm alone
 Risk controls for data collected by MD-based medical devices should include
process activities through the data lifecycle such as ensuring the data is complete,
correct, and consistent (affecting data integrity), as well as ensuring data is the best
representative data at that time.
 Operational risk controls are features in the software itself that directly interact with
the user (e.g., human oversight.).
 Design of the human user interface should be reviewed to ensure this does not
introduce bias or unduly influence the user.
 For autonomous systems, there might be a need for a hand-off strategy where
control is passed from the system to the user.
 Risk management review according to Clause 9 of ISO 14971:2019 and essential
principles 3,4,5 and 8 should be performed before commercial release of the ML-
based medical device
Source: AAMI CR34971:2022, Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine

Learning,

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Quality Management Systems

The AI/ML device shall be designed and manufactured, and monitored in accordance with
Medical Devices Quality Management System (ISO 13485) to document and implement
all processes, reduce mistakes, and guarantee continues quality and safety of the device.
The Quality Management System in place shall ensures compliance with this Regulation.

QMS and Regulatory requirements: The organization, which designs and deploys the
AI/ML, is responsible for implementing the QMS, which include developing a quality
policy, quality objectives, procedures, and project-specific plans that are customer focused.
It is also required to provide the appropriate level of resources (including people, tools,
environment, etc.), needed for ensuring the effectiveness of the AI/ML lifecycle processes
and activities in meeting SFDA regulation and customer requirements.

Human resources: It is important to ensure that personnel who are assigned to AI/ML
projects should be competent in performing their jobs. For AI/ML, such a team should have
competencies in technology and software engineering including an understanding of the
clinical aspects of the use of the software.

Infrastructure: Such as equipment, information, communication networks, tools, and the

physical facility, etc., should be made available throughout AI/ML lifecycle processes.
Such infrastructure is used to support the development, production, and maintenance of
AI/ML and consequently needs to be provided and maintained.

Traceability: The QMS shall assist the organization to produce a systematic documentation
of the AI/ML and its supporting design and development, including a robust and
documented configuration and change management process, and identifying its constituent
parts, to provide a history of changes made to it, and to enable recovery/recreation of past
versions of the software, i.e., traceability of the AI/ML.

Measurement and Monitoring: Post market surveillance including monitoring,

measurement and analysis of quality data can include logging and tracking of complaints,
clearing technical issues, determining problem causes and actions to address, identify,
collect, analyze, and report on critical quality characteristics of products developed.

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Aspects important for the measurement, analysis, and improvement of AI/ML processes
and products can include:

- Evaluation of the AI/ML and its lifecycle processes should be based on defined
responsibilities and predetermined activities including using leading and lagging
safety indicators and collecting and analyzing appropriate quality data.

- Corrections and corrective actions may be required when a process is not correctly
followed or the AI/ML does not meet its specified requirements.

- Nonconforming AI/ML should be contained to prevent unintended use or delivery.

The detected nonconformity should be analyzed and actions taken to eliminate the
detected nonconformity (i.e., correction); and to identify and eliminate the cause(s)
of the detected nonconformity (i.e., corrective action) to prevent recurrence of the
detected nonconformity in the future.

- Actions taken to address the cause of AI/ML nonconformities, as well as actions

taken to eliminate potential AI/ML nonconformities, should be verified/validated
before AI/ML release and should be evaluated for effectiveness.

- Lessons learned from the analysis of past projects, including the results from
internal or external audits of the AI/ML lifecycle processes, can be used to improve
the safety, effectiveness, and performance of AI/ML. The manufacturer should also
have processes in place for reporting adverse events to the SFDA, the collection of
active and passive post market surveillance information in order to make
appropriate decisions relating to future releases.

- After the product is in the market, it is important to maintain vigilance for

vulnerability to intentional and unintentional security threats as part of post market
surveillance.

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Change Notification

 According to SFDA “Requirements for Medical Devices Marketing Authorization

(MDS-REQ 1)” , the SFDA shall be informed, via the electronic system “GHAD”,
within (10) days of the occurrence any significant change to the relevant information
or (30) non-significant change".
 Major/Significant change: It could reasonably be expected to directly affect the safety
or effectiveness of a device.
 Minor/Non-significant change: It could reasonably be expected to indirectly affect the
safety or effectiveness of a device.
 Manufacturer shall have procedures within the manufacture’s QMS for evaluating the
changes and shall cover:
 Change control
 Categorizing the changes as significant or not.
 Informing the SFDA of the changes.
 All changes shall be evaluated, verified and validated according to the accepted
procedures in the manufacturer’s QMS
 Changing that could not reasonably be expected to affect the safety or effectiveness of
a device shall be updated at the time of renew the MDMA certificates.

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 Manufacturer shall fill the below form and submit it to SFDA:

Table 1 Version Control Method

Medical Device Model:
Description of Change Major or Date and Reason for Relevant
Minor changes Document
1

For more information, kindly see the SFDA Guidance on MDMA Significant and Non-
Significant Changes.

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 Annexes

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 Annex (1): Definitions & Abbreviations

KSA Kingdom of Saudi Arabia

SFDA Saudi Food and Drug Authority
MDS Medical Devices Sector
Manufacturer Means any natural or legal person with responsibility for design and
manufacture of a medical device with the intention of making it
available for use, under his name; whether or not such a medical
device is designed and/or manufactured by that person himself or on
his behalf by another person.
Authorized Means any natural or legal person established within the KSA who
Representative has received a written mandate from the manufacturer to act on his
(AR) behalf for specified tasks including the obligation to represent the
manufacturer in its dealings with the SFDA.
Artificial Technology that realizes some or all of intellectual abilities
Intelligence (intelligence) of human such as recognition and learning based on
(AI) methods including machine learning using a computer
AI-based Medical devices that support the work for medical professionals by
Medical diagnosing, managing or predicting diseases based on analysis of
Devices medical big data with AI technology
Artificial Engineered system that generates outputs such as content, forecasts,
intelligence recommendations or decisions for a given set of human-defined
system (AI objectives.
system)
Machine is an artificial intelligence technique that can be used to design and
Learning (ML) train software algorithms to learn from and act on data. Software
developers can use machine learning to create an algorithm that is
‘locked’ so that its function does not change, or ‘adaptive’ so its
behaviour can change over time based on new data.
Genetic Algorithm which simulates natural selection by creating and evolving
algorithm GA a population of individuals (solutions) for optimization problems.
(ISO)
Machine Algorithm to determine parameters of a machine learning model from
learning data according to given criteria.
algorithm

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Reference It is a result of checking whether a certain disease or condition wants
Standard to diagnose or predict exists or not
Prospective It is a method to trace changes for a certain period of time after pre-
study setting factors (risk factors) to be studied, observing the changes
caused by risk factors.
Retrospective It is a method of conducting a study without direct contact with study
study subjects. It is a clinical trial conducted to verify the safety and
effectiveness of medical devices using medical data of subjects
obtained through previous medical care or clinical trials rather than
recruiting subjects.

Further Relevant Reading Materials

o Software as a Medical Device (SaMD): Key Definitions (IMDRF/SaMD
WG/N10FINAL:2013)
o Software as a Medical Device (SaMD): Possible Framework for Risk Categorization and
Corresponding Considerations (IMDRF/SaMD WG/N12FINAL:2014)
o Software as a Medical Device (SaMD): Application of Quality Management System
(IMDRF/SaMD WG/N23 FINAL:2015)
o Software as a Medical Device (SaMD): Clinical Evaluation (SaMD WG (PD1)/N41R3)
o Guideline on Review and Approval of Artificial Intelligence(AI) and big data-based
Medical Devices (For Industry), Republic of Korea, Ministry of Food and Drug Safety.
o Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine
Learning (AI/ML)-Based Software as a Medical Device (SaMD), FDA.

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