Webinar on AI in Medical Diagnosis with Emerging Technologies

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1 www.bisresearch.com
Future of AI in Medical Diagnosis with Emerging Technologies
Future of AI in
Medical Diagnosis
with Emerging
Technologies
Webinar April 2023

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Speakers
Paul Fletcher Dyer
Global Compliance Specialist
in AI Medical Device Software
Cognetivity Neurosciences Ltd
Shreya Srinivas
Research Analyst
BIS Research Inc.
Swati Sood
Principal Analyst
BIS Research Inc.

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Agenda
 To evaluate policy implications related to
AI and digital transformation in
healthcare.
 To analyze the initiatives taken and
regulations implemented to address
cybersecurity concerns.
 To analyze the major factors driving and
hindering the growth of AI in medical
devices diagnosis
 To evaluate the major players in the
ecosystem
 Q&A Session

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Overview: AI in Healthcare

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Relevancy of AI in Healthcare
Tremendous Amount of
Healthcare Data
• Data in healthcare is generated
through various sources such
as electronic health records
(EHR), medical-related
smartphone apps, and
wearable devices.
• AI has the potential to manage,
analyze, and interpret such
huge amounts of data.
Growing Prevalence of Chronic
Diseases and an Aging
Population
• There has been an increase in
the geriatric population globally.
• Implementation of AI in
healthcare can help improve the
speed and accuracy of
diagnosis and treatment of
disorders, thus helping to
alleviate the rising burden on
the healthcare system.
Growing Demand for Precision
Medicine
• AI has the ability to analyze big
data in a way that can enable
healthcare providers to uncover
new insights and tailor
treatments as per an individual.

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AI’s Role in Assisting the Various Stakeholders in Healthcare Settings
Patients
• At-home assistance
• Remote patient
monitoring
• Medication adherence
• Chatbots to converse
and conduct preliminary
diagnosis
Clinicians
• Augment healthcare
workforce
• Aid physicians in areas
such as early detection
and diagnosis by
identifying markers such
as tumors and lesions
• Predictive analysis of a
patient's health
• Provide clinical-decision
support
• Help reduce diagnostic
errors
• Take on administrative
tasks to enable the
clinician to focus on the
patient
Global Population
• Map the spread and
burden of diseases
• Predictive analysis can
be used to understand
future disease spread
• Enable governments
and organizations to
react swiftly and
allocate timely
resources to prevent or
assuage disease
outbreaks
Pharmaceutical,
Biotechnology, and
Medtech Companies
• Can model unknown
cellular mechanisms to
aid in discovery
• Can help select the
most suited clinical trial
for a patient
• Can significantly reduce
drug discovery costs by
streamlining the
discovery process

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Different Types of AI/ML Algorithms in Healthcare
Support Vector Machine
(SVM)
Neural Network (NN)
K-Nearest Neighbor
(KNN)
Naïve Bayes (NB)
Decision Tree (DT) Random Forest (RF) Logistic Regression

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Why AI?

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Why AI?
 Firstly, AI is not a single entity, rather it is a
multitude of programs, a ‘hydra’ if you will.
AI’s in pharmaceutical are research tools
 Medical Devices replace a biological
function, AI does not do this. AI Medical
Devices are ‘Limited Memory AI’s’
 There are AI’s in imagery, auditory, data
analyses, cognitive, gait to name a few

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Can AI diagnose?

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Can AI Diagnose?
 An AI cannot diagnose
 But that argument is becoming increasingly
redundant.
 And in the space of previously difficult to
monitor fields (e.g. Dementia, Parkinson’s),
AI’s can provide greater information to
HCP’s and can be seen as providing a
diagnoses. Why this statement?

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AI: The Next Big Thing in Medical
Imaging

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Growing Number of FDA Approvals for AI-Enabled
Medical Imaging Solutions
2 1 2 5
11 15
39
51
90
97
79
0
20
40
60
80
100
120
AI is already transforming the field of medical
imaging and is expected to continue to grow
at a significant pace
AI: The Next Big Thing in Medical Imaging

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What are the factors driving and
restraining the adoption of AI/ML-enabled
solutions for medical diagnosis?

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Factors Propelling the Demand for AI in Medical Diagnosis
Growing Installed Base of
Diagnostic Equipment in
Regions such as the Asia-Pacific
Increasing Funding to Emerging
Digital Health Startups to Support
Research and Advancement of AI-
Enabled Solutions
Ability to Shorten Wait Times and
Reduce the Burden on the Healthcare
System, especially in LMICs
According to data published by the
Organization for Economic Co-
operation and Development (OECD),
Japan and Australia have the highest
number of CT scanners globally, at
nearly 116 and 70 CT scanners per
one million inhabitants.
Steep growth in the number of
funding activities from 2019/2020 to
2021/2022.
The funding activities were aimed at
advancing, developing, and
commercializing the product.
AI can help alleviate challenges in
LMICs such as the inequitable
distribution of healthcare resources
and high workload on healthcare
professionals due to the dearth of
physicians per million population.

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Factors Restraining the Demand for AI in Medical Diagnosis
Lack of Transparency in AI
Algorithms
Cybersecurity Concerns Around
Data Sharing
Challenges Related to Identification
and Minimization of Bias in AI
Algorithms
The difficulty in explaining and
interpreting ML models is a key
factor leading to the lack of
transparency. This can lead to a
lack of understanding of how
clinically important decisions are
made and the basis behind them.
While the lack of available data to the AI
system can potentially lead to higher
chances of inducing bias, it also raises
concerns relating to data sharing among
third-party sources.
Organizations such as HIPAA in the U.S.
and the General Data Protection Regulation
(GDPR) in Europe are responsible for
regulating data privacy regulations.
Both intrinsic and extrinsic biases can
drastically affect clinical outcomes.
The bias in AI algorithms due to the
underrepresentation of certain sections
of the population can skew the outcome
presented by the AI-enabled medical
device, therefore leading to chances of
incorrect diagnosis of diseases.

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Who is developing AI for healthcare?

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Who is Developing AI for Healthcare?
Large medical device
manufacturers are
developing their own
software, however they will
follow the acquisition
pathway for novel software.
Innovative AI is done by
small IT start-ups and
primarily focus upon the
technology, not the MDR
evidence requirements whilst
developing the software.
This leads to conflict
between software
development needs and
medical device development
practices when attempting to
enter the market.
There is also a lack of
expertise in the field

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What are the possible solutions?

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What are the Possible Solutions?
There needs to be a
fundamental shift in how AI
is perceived in healthcare,
and especially its
regulation. I believe that
the first step is separating
the use of AI in the
healthcare sectors,
primarily its use in
pharmaceutical
development and clinical
diagnoses.
From this separation,
properties such as the use
of AI outputs, data
protection, training sets
and clinical evidence can
be more easily addressed.
From this separation,
properties such as the use
of AI outputs, data
protection, training sets
and clinical evidence can
be more easily addressed.
 Is there a solution? In my opinion, yes
 But not in the current climate.

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What are the key development activities
being witnessed?

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Funding Scenario
 The demand for AI-enabled medical devices
is mainly driven by startups and mid-sized
companies, along with large corporations.
Many of the startups have raised a substantial
amount of funding for the development and
commercialization of their AI-enabled medical
device.
 Companies such as Paige.AI, Aidoc, Volta
Medical, Subtle Medical, Inc., and Qure.ai
underwent the highest number of funding
rounds during the time period January 2018-
December 2022. Source: BIS Research Analysis
With the growing demand and advancement of AI-based technologies, investors are
readily investing in digital healthcare companies developing AI-enabled solutions
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6
26
17
0
5
10
15
20
25
30
2019 2020 2021 2022
Number
of
Funding
Activities

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Partnerships and Collaborations
Aidoc has the maximum number of partnerships/collaborations with hospitals and other
companies
Hospitals Other Companies

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What does the global market ecosystem
look like?

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Market Ecosystem
 Some of the established players in the market
ecosystem include GE Healthcare, Canon Inc.,
Siemens Healthineers AG, Koninklijke Philips
N.V., and Samsung Group.
 The rest are emerging digital healthcare startups
and other emerging companies offering AI-
enabled medical devices and software for
disease diagnosis.
The global market for ‘AI in medical imaging/diagnosis’ is highly fragmented.

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Market Ecosystem: Startups Founded in the period 2018-2022
U.S.
Asia-Pacific Europe

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Solutions Collaborations
Insights Marketplace Embedded AI
Access CT 32
Slice
Ingenia Prodiva
Dura Diagnost
PerformanceBridge
IntelliSpace
VitalEye
HealthSuite
Augmented
Intelligence
Cognitive
Intelligence
Adaptive
Intelligence
Secure All-in-One Data
Current
Future Ready
Market Capitalization Strategy

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Solutions Collaborations
Continuous Innovation and
Strategic Partnerships
Development of Innovative
Technologies
Aquilion One
Feb 2023: Canon Medical partnered with Tribun
Health to extend clinically intelligent AI-driven
enterprise platform to include pathology
Vantage Galan
Aquilion Lightning
Aquilion Exceed
Aquilion Prime
Aplio ultrasound
Vantage Orion
Vantage Fortian
Nov 2022: Canon launched Orian SP and Galan
SP, featuring AI-driven workflow solutions.
Advanced intelligent
Clear-IQ Engine (AiCE):
Initially developed for CT,
this denoising deep
learning technology was
soon deployed across
MRI and PET/molecular
imaging to help speed up
scan times and improve
image quality.
Automation Platform
and AUTOStroke
solution: designed to
enhance clinical
confidence and
streamline workflows
with the power of deep
learning technology.
Altivity:
Consolidates a
range of DL and
ML technologies
to help clinicians
deliver better
outcomes
Current
Future Ready
Market Capitalization Strategy

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What is the regional market landscape?

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Regional Market Landscape
Each region has a wide incremental opportunity to grow during the period 2022-2032
3,124.6 4,011.7
35,458.2
2021 2022 2032
$Million
North America Europe Asia-Pacific Rest-of-the-World
CAGR (2022-2032): 24.35%
45.82%
28.23%
12.76%
13.19%
Market Size in 2021: $3,124.7 Million
55.36%
26.39%
6.78% 11.47%
Market Size in 2032: $35,458.2 Million
Radiology
Cardiology
Hematology
Others
A vast majority of approved AI-enabled medical devices are for radiological applications

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What was the COVID-19 impact on the
adoption of AI in healthcare and medical
diagnosis?

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COVID-19 Impact
 The adoption of AI in clinical settings
increased during the pandemic owing to the
numerous advantages of AI in tackling
COVID-19.
 AI assisted in achieving high execution in
diagnosis, prognosis evaluation, pandemic
prediction, and drug discovery for COVID-
19.
 Moreover, wearable biosensors and
machine learning-based solutions aided
remote monitoring of COVID-19 patients
having a positive impact on the market.
The COVID-19 pandemic had an overall
positive impact on the adoption of AI in
medical diagnosis

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What is the current situation in healthcare?

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What is the Current Situation in Healthcare?
 AI medical technology is released under the
terms of the MDR.
 There are lot’s of guidance documents being
issued
 Manufacturers are left with a simple question
‘what do we need to do legally?’. Everything else
becomes ‘client requirements’ and are not law.
 Manufacturer’s are left with a minefield of
requirements for a single Client, let alone a
Healthcare Market
 The Healthcare Market isn’t much better
 Healthcare markets are facing an influx of novel
products and data sources for both established
pathways and pathways that do not yet exist.
 Multiple parties are trying to address the impact
of AI, but it is leading multiple fractured thoughts
and processes i.e. with no overall regulation,
each HCP / Clinics / Hospital / Trust etc are
interpreting the need for AI without an overall
combined strategy
 Ultimately, the current state of affairs is such that
very few patients are able to access the benefits
of AI Medical technology

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What is the future impact on the market?

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What is the Future Impact on the Market?
 There will be a period of flux and confusion
 New legal requirements will impact AI
medtech & the healthcare market
 The result?
 Drop in available software as companies
cannot provide the evidence for new regs
 New patient pathways being abandoned due
to software being removed.

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Future of AI in Medical Diagnosis with
Emerging Technologies
Guest Speaker Contact Information
Paul Fletcher Dyer
Reach out to him at info@paul.fletcher-dyer@mail.com

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Webinar on AI in Medical Diagnosis with Emerging Technologies