A presentation where content is laid out in a research format to give insights on role of artificial intelligence in Laboratory services in healthcare sector

1. Artificial Intelligence
in Laboratory
Services

2. CONTRIBUTORS
Group –1
Team members Roll no.
• Akansha Singh 0873
• Purnima Rathore 0965
• Shruti Aggarwal 1000
• Shweta Bisht 1001
• Shweta Shastri 1002

3. INTRODUCTION
• Clinical laboratories are healthcare facilities that offer a broad variety of laboratory techniques
that support the doctors in diagnosing , treating and handling patients.
• The clinical laboratory industry has come a long way from conventional manual processes to
automation infused with AI.
• Laboratory automation dates back to the 1950s and its history includes the evolution of
laboratory equipment, the growth of laboratory information (management) systems (LIS / LIMS)
and pre- and post-analytic automation advancement.
• The first automated instrument was introduced in 1957 and this instrument was the
Autoanalyzer I
• Laboratory information system began taking shape in the late 1970s which introduced the
electronic data management into laboratory setting.
• Eventually all phases of testing were ultimately combined in Total Laboratory Automation. In 1980
the first fully automated laboratory was established.

4. INTRODUCTION
• The automation of most of the functional components of the analytical systems in
a clinical laboratory has allowed it to grow and evolve into a system approach.
• Clinical Labs have started exploring the use of machine learning ( ML) to alleviate the pressure of
rising service demand and boost quality and health.
• With the introduction of programmable software it became possible to introduce great flexibility
into the system, both in the operational processes and in the feedback loop and that is how AI
gained traction in clinical laboratories.
• Rules-based programming has replaced many human tasks and judgment decisions in the clinical
laboratory, accelerating workflow and reducing errors.
• A majority of laboratory executives and managers predict AI will be essential to the
diagnostic laboratory in the coming 4-5 years.
.

5. INTRODUCTION
• The Global Clinical Laboratory Test Market is expected to register 6.5% CAGR to reach USD
324508.8 Million by 2022.
• Standalone centers dominate the diagnostic space with a 47% share, while hospital-
based laboratories have a 37% market share.
• Growing adoption of deep learning in various healthcare applications, especially in the areas of
medical imaging, disease diagnostics, and drug discovery, and the use of different sensors and
devices to track a patient’s health status in real time are supplementing the growth of the market.

6. AI in Indian Healthcare
• Adoption of Artificial Intelligence is reshaping the Indian healthcare market significantly.
• AI-enabled healthcare services like automated analysis of medical tests, automation of
healthcare diagnosis with the help of monitoring equipment, and wearable sensor-based
medical devices, are revolutionizing the medical treatment processes in the country.
• The capability of AI applications to improve doctor efficiency will help in tackling
challenges like uneven doctor-patient ratio, by providing rural populations high-quality
healthcare, and training doctors and nurses to handle complex medical procedures.
Government initiatives:
• National eHealth Authority (NeHA) - An authority which is responsible for the expansion of
the integrated health information system within India.
• The Information Technology Act (2000) & Rule (2011) - mandate that service providers
and patients exchange information constantly by using the latest technologies.

7. Future of AI in Indian Healthcare
• GOI announces an increase in health budget by 10% (taking it to 70k crore) and stated
the key part of AI in Indian Healthcare.
• Tuberculosis screening using AI: AI deep learning-based algorithms detects signs of TB in
X-rays in few seconds! E.g. Rajasthan & Chennai.
• Automatic report of Scans: Automatic report generation for radiology scans by AI. AI
algorithms combines the diagnosis of disease along with description of findings and
generates the text reports.
• Real-time quality monitoring: High quality comes at high cost is being challenged by the
advent of AI. Now, with AI every scan can be double checked.
• Cancer Screening: AI is transforming the entire landscape of cancer screening as the
algorithms are getting improved.

8. OBJECTIVE
GENERAL OBJECTIVE:
• To study the strength and shortcoming of
Artificial Intelligence technology in the
clinical laboratory diagnosis.
SPECIFIC OBJECTIVE:
• To know how AI is revolutionizing the
diagnosis in laboratories.
• To know the valuable impact of AI in
diagnostic process in this decade.
• To get to know about the barriers in
adoption of AI in India.

9. METHODOLOGY
Online repositories such as ScienceDirect, Google Scholar,
and PubMed were used with a collection of keywords to
search for papers and publish the literature.
These keywords provided access to all the literature
describing this study's framework.
The first set of keywords was used in clinical laboratories
to associate with the different references to Artificial
Intelligence. Furthermore, machine learning was also used
in the same context as it is a subset of Artificial
intelligence. Lastly, automation was also looked up, as AI
was used partially or entirely in automated laboratories.
Articles found: 73
Articles focusing on a single laboratory procedure was
eliminated as a collective view is needed. The keywords
were used in tandem with the OR and AND corresponding
Boolean Operators. All fields that included titles,
abstracts, subject headings such as MeSH and general
keywords were searched.

10. METHODOLOGY
Query Keywords (that searched
All Fields i.e. within titles,
abstracts, subject headings
like MeSH, and general
keywords)
1. "Artificial Intelligence" OR
"AI" in clinical Laboratories
2. "Machine learning" OR
"Automation" in clinical
laboratories
Final Search included 1 AND 2

11. METHODOLOGY
Inclusion criteria
• The stage of publishing was not taken into
account. Published, unpublished and pre-prints
articles were included.
• It covered all articles written in English
• It included all the open access journals.
Exclusion criteria
• Articles which did not meet any of the
requirements for inclusion set out above were
excluded. This removed all searches that were
categorized into categories other than full-text
posts.
• All articles focusing on drug efficacy or
medical procedures relating to laboratories were
excluded.

12. REVIEW OF LITERATURE
Journal &
Date
Title Authors & Link Objective Key findings
American Society for
Microbiology Journals
25 MAR 2020
Machine Learning
Takes Laboratory
Automation to the
Next Level
Bradley A. Ford, Erin
McElvania
https://doi.org/10.1128/JCM.
00012-20
• To evaluate the performance of
automated image analysis software to
screen urine cultures.
• To understand challenges like
workload, understaffing without any
automation system
• Automation decreases pre-
analytical error & improves
consistency of repetitive task.
• By using AI screen and image
analysis software, results are
auto verified.
AACC
MARCH 01,2019
Machine Learning
and Laboratory
Medicine: Now and
the Road Ahead
Thomas J.S. Durant
• To understand the use of machine
learning in clinical laboratory
• Does ML have potential to improve the
quality service provided by
Laboratory?
• What are the barriers for development
and adoption of ML in Clinical
Laboratories?
• The main utility of ML for a
broad array of datasets, such
as analyzing erythrocyte
morphology, bacterial
colony thyroid panels, urine
steroid profiles, flow
cytometry, and to review test
result reports for quality
assurance.

13. Journal & Date Title Authors & Link Objective Key findings
Future Healthcare journal
June 6, 2019
The potential for
artificial intelligence
in healthcare
Thomas Davenport
https://www.ncbi.nlm.nih
.gov/pmc/articles/PMC66
16181/
• What are different types of AI
technologies relevant to
healthcare field?
• Can AI-based capabilities be
effective in personalizing and
contextualizing care?
• Will AI systems replace
pathologist and clinicians?
• Patient Enagegement & adherence
applications
• 300 clinical leaders & healthcare
executives out of them
70% respondents report 50%
patients highly engaged while 42%
said less than 25 % patients were
engaged.
• Speech & text recognition for patient
communication and clinical notes
capture
• AI image analysis – Pathology images
will be examined by machine learning
• Use of AI aid allows pathologist to
provide high performance / high
complexity in tissue analysis.
• AI will have a hard time increasing
performance from 95 to even 99%
• Replacement for pathology is system
where both humans and computer
work together.
Elsevier
4 February 2000
Use of artificial
intelligence in
analytical systems
for the clinical
laboratory
https://doi.org/10.1016/
0009-9120(95)00002-Q
• To consider the role of software in
system operation, control and
automation, and attempts to
define intelligence.
• Applications of AI in stand-alone
systems for knowledge engineering
and medical diagnosis and in
embedded systems for failure
detection, image analysis, user
interfacing, natural language
processing, robotics and machine

14. Journal & Date Title Authors & Link Objective Key findings
Siemens Healthcare
July 2018
How artificial intelligence will
change the clinical laboratory
Siemens healthcare How AI improves costs in
Clinical laboratory?
Does AI improve patient
experience?
Does AI improve efficiency of
tasks?
• A collective human and AI
effort achieves greater
success than either alone.
• An AI based laboratory
system improves the
efficiency and lowers the
cost.
• Some form of automation
in laboratories has
reduced manual labour
• AI is expected to
completely permeate the
laboratory market soon.
Healthmanagement.org
volume 18 Issue-3
2018
AI and healthcare technology
in India: opportunities,
challenges, and emerging
trends
Dr. Pooja Rao
https://healthmanagement.or
g/c/healthmanagement/issuea
rticle/ai-and-healthcare-
technology-in-india-
opportunities-challenges-and-
emerging-trends
What are the challenges being
faced by India in healthcare
technology?
What are the unique
opportunities of AI and
healthcare technology in
India?
What are the emerging trends
in the field of healthcare
technology?
• Scaling up and distributing
technology in India is
challenging.
• Lack of Govt spending on
healthcare means public
health programs are still
largely funded.
• With rapid penetration of
smartphones and internet,
support for PPP model and
govt enthusiasm is high for
innovation and locally
made technology in

15. RESULTS
APPLICATIONS CHALLENGES OPPORTUNITIES
• Decreases pre analytical
errors
• Auto verified results
• Improve turnaround time
• Personalized medicine
• Better documentation
• Image analysis
• Increase efficiency
• Bias in diagnostic
processes
• Could machine
learning leads to
machine decision
making?
• Legal issues
• Manpower
• Cost
• Automated urine
analysis system
• Neural network
• Clinical decision
support system
• Predicting the
progression of disease

16. RESULTS
• Innovative, sustainable and scalable artificial
intelligence technology has the potential to greatly
improve health outcomes.
• AI applications being developed and deployed
include algorithms that analyze X rays, read ECGs,
spot abnormal patterns and automatically scan
pathology slides .
• Instead of replacing doctors, AI algorithms might
work best alongside them in healthcare. AI and
machine learning software are beginning to
integrate themselves as a tool for efficiency and
accuracy within clinical laboratories.

17. REFERENCES
• Naughler. C, Church D, Automation and artificial intelligence in the clinical laboratory, 56(2):98-110, Crit Rev Clin Lab Sci.
2019 https://pubmed.ncbi.nlm.nih.gov/30922144/
• Thomas J.S. Durant, Machine Learning and Laboratory Medicine: Now and the Road Ahead, As artificial intelligence
proliferates, clinical laboratorians can leverage their expertise in validating new technology to improve patient care,
American Association of Clinical Chemistry March
2019 https://www.aacc.org/publications/cln/articles/2019/march/machine-learning-and-laboratory-medicine-now-and-
the-road-ahead
• Place. J, Truchaud.A, Ozawa.K, Pardue.H, Schinepelsky.P, Use of artificial intelligence in analytical systems for the clinical
laboratory, International Federation of Clinical Chemistry, Committee on Analytical Systems,42,
2000 https://pubmed.ncbi.nlm.nih.gov/18924784/
• Ford BA, McElvania E. 2020. Machine learning takes laboratory automation to the next level. J Clin Microbiol 58:e00012-
20. https://doi.org/10.1128/JCM.00012-20.
• Davenport T, Kolata R. Potential for artificial intelligence in healthcare. Wellesley, USA Future healthcare
journal:2019 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/
• Rao P.AI and healthcare technology in India: opportunities, challenges, and emerging trends. Mumbai , India. Health
management, volume –18 issue 3,2018 https://healthmanagement.org/c/healthmanagement/issuearticle/ai-and-
healthcare-technology-in-india-opportunities-challenges-and-emerging-trends%E2%80%8B
• Menon P. Effect of artificial intelligence in the clinical laboratory. Thumbay labs, Thumbay medicity, Ajman UAE. Jan
2020 https://insights.omnia-health.com/laboratory/effect-artificial-intelligence-clinical-laboratory
• Rosseti. M, Kumar. A, Felder. R, Mobile robot simulation of clinical laboratory deliveries, 1998 Winter Simulation
Conference. Proceedings , Washington, DC, USA, 1998, pp. 1415-1422 vol.2,
WSC.1998. https://ieeexplore.ieee.org/abstract/document/746010/citations#citations