1. Guide: 1. Yamini D. Shah
M. Pharm, Ph.D.,
Professor & H.O.D of
Pharmaceutics and
Pharmaceutical Technology
ARTIFICIAL INTELLIGENCE IN
HEALTHCARE
Department of Pharmaceutics and Pharmaceutical Technology
. M. College of Pharmacy, Opp. Gujarat University, Navrangpura, Ahmedabad, Gujarat, India
Prepared by:
Shailvi Soni
Enrollment
no.:192280820012

2. Table of content
Introduction
Introduction to healthcare data
How AI can help in healthcare…
Application of
AI in
healthcare
Clinical care
AI in Radiology
AI in Dermatology
AI in Pathology
AI in Drug Development
AI in Cardiology:
AI in Medicine:
AI in Neuroscience
Ai in cancer
Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
in healthcare

3. Introduction
 Artificial Intelligence is a human intelligence
research and prototype where a wise specialist is
a system that knows its situation and conducts
activities that amplify its risk of development.
 Artificial intelligence is not new, but the field has
recently made rapid progress. To a limited extent,
this has been empowered by advances in
registering power and the tremendous volume of
currently generated computerized knowledge.
 Significant innovation organizations, including
Google, Microsoft and IBM, are pouring
resources into the development of AI for
medicinal services and research.

4.  We understand that machines will not be replaced
by human doctors within a reasonable time period,
but AI may help doctors decide on better clinical
decisions or even supplant human judgement in a
certain utilitarian zones of medicinal services (e.g.,
radiology or neurology).
 The rising accessibility of healthcare information
and the rapid improvement of big data analysis
methods have made AI's ongoing productive
applications in medicinal services conceivable.
 Incredible AI techniques, led by critical clinical
investigations, will open up clinically relevant data
covered in the enormous amount of knowledge,
Introduction

5.  The use of AI (machine learning) ML strategies,
including Artificial Neural Networks (ANNs), is
another way to deal with AI.
 Using an ANN approach, computer commands
create an artificial "neurons" decision-making
system that works in ways comparable to organic
sensory systems.
 In medical science healthcare, ML-fueled clinical
decision support system (CDSSs) had been
proposed for picture survey and translation for
radiology, dermatology, and pathology, and for
improving the extension and precision of
biomedical sign interpretation.
Introduction

7. Introduction to Healthcare
data:
 Healthcare data should be 'trained' by knowledge
generated from clinical activities, such as
screening, commitment, treatment task, etc.,
before AI structures can be sent in healthcare
applications, in order to learn comparable
subjects meetings, relationships between subject
highlights and premium outcomes.
 This clinical information often exists in the form of
socioeconomics, restorative notes, electronic
medical gadget accounts, physical examinations,
and clinical laboratories and images, which are
not yet limited.

8. How AI can help in
healthcare…
 Better healthcare results: Enable doctors to be
diagnosed with increasingly accurate diagnostics
and concentrate on avoidance skills. (9)
 Assure medical drug safety: Empower
pharmaceutical manufacturers to quickly assess
the consistency, adequacy and protection of new
product expectations. (9)
 Get new treatments for faster recovery and market
growth: Clinical preliminaries of Quicken using
sources of certifiable knowledge.(9)
 Control costs: Improve medical clinics' clinical
presentation by improving staffing assets and
making a change to reduce the number of

9. APPLICATIONS OF AI IN
HEALTHCARE
1. CLINICAL CARE: The use of AI to examine clinical
information, research publications, and expert rules
could inform treatment choices as follows:
 Medical imaging – Therapeutic outputs have been
collected and put away methodically for quite a while
and are readily available for the preparation of AI
systems. The cost and time associated with examining
could be reduced by AI, conceivably allowing more
likely target treatment. In identifying conditions like
pneumonia, skin and breast cancer, and eye ailments,
AI has shown promising results.
 Echocardiography – Tested in research, the Ultromics
framework utilizes AI to investigate echocardiography

10.  Screening for neurological conditions – AI
systems are being created to analyze samples to
predict insane scenes and detect and track side
effects of neurological disorders, example used in
Parkinson's infection.
 Surgery- AI-constrained automated tools have been
used in research to complete complex tasks in
keyhole surgery, such as tying knots to close
injuries.
 Image Based Diagnosis: As of now the best field
of medical AI applications is potentially
computerized restorative picture analysis.
Numerous restorative fortes depend on picture-
APPLICATIONS OF AI IN
HEALTHCARE

11. 2. AI in Radiology:
APPLICATIONS OF AI IN
HEALTHCARE

12. 3. AI in Dermatology:
 A major role in the diagnosis of various forms of
skin injury like skin melanoma.
 Skin Image Analysis: Using AI and PC vision,
organizations create applications and gadgets to
analyses images to anticipate and prevent the
occurrence of skin sickness.
 Skincare Treatment Personalization:
Companies are building a suggestion engine to
customized skincare treatment recommendations
to the form of skin consumer.
APPLICATIONS OF AI IN
HEALTHCARE

13. 4. AI in Pathology:
APPLICATIONS OF AI IN
HEALTHCARE

14. 5. AI in Drug Development: : Some
Applications of AI in Drug Development are
as follows:
 AI in synthesis of drug-like compounds
 AI in drug repurposing
 AI in selection of a population for clinical trials
 AI in finding the hit or lead
APPLICATIONS OF AI IN
HEALTHCARE

15. 6. AI in Cardiology:
APPLICATIONS OF AI IN
HEALTHCARE

16. 7. AI in Medicine:
 Machine Learning (also known as Deep Learning)
speaks to the virtual component that is addressed
through numerical calculations that enhance
learning through experience.
 There are three types of AI calculations:
(i) unsupervised (capacity to discover designs),
(ii) supervised (past model-dependent grouping
and forecast algorithm), and
(iii) fortification learning (utilization of arrangements
of remunerations and disciplines to frame a
technique for task in a particular issue space)
APPLICATIONS OF AI IN
HEALTHCARE

17.  In an assortment of prescription courses, artificial
intelligence may be used. Four models are given here:
I. Clinical information analysis: The ability of artificial
intelligence to process common language allows it to
peruse clinical content from any source and distinguish
therapeutic and social ideas from order and code.
II. Knowledge for patient information: It explains the
historical context of their consideration of patient’s
problems and may provide a medical record with a
psychological synopsis.
III. Patient similarity: As compared to static patient
partners, AI helps scientists to make complex patient
accomplices. It also allows an appreciation of which
treatment approach fits best for a particular group of
patients.
IV. Therapeutic section of knowledge: Experts can
uncover material in unstructured medical writing with AI
innovations to endorse hypotheses, helping to expose
APPLICATIONS OF AI IN
HEALTHCARE

18. 8. AI in Neurosciences: AI In neurological OT
(Occupational Therapy):
 Currently, it is recognized that cerebral tumors
cause a significant redesign of the functional
system. Useful enrollment has been helped by
simulated intelligence showing greater optimum
percentage than anatomical enlistment.
 Brain tumor removal has been accepted as a
perfect robotic surgery technique. It involves a
view of the cell by means of an automated system
and a subsequent adaptation of its behavior to
evolving natural parameters.
APPLICATIONS OF AI IN
HEALTHCARE

19. 9. AI in Cancer:
 Artificial Intelligence (AI) take incredible parts in
the subjective translation by master clinicians of
malignant growth imaging, including volumetric
outline of tumors after some time, extrapolation of
the tumor genotype and natural course from its
radiographic phenotype, clinical outcome
prediction, and assessment of the impact of
infection and treatment on contiguous organs.
 In the underlying translation of images, artificial
intelligence will be in robotize forms but the
executive decisions on whether to monitor a
mediation, and the subsequent interpretation to a
worldview that has yet to be conceived.
APPLICATIONS OF AI IN
HEALTHCARE

20.  AI Applications in Cancer Imaging:
APPLICATIONS OF AI IN
HEALTHCARE

21. Similar advance approaches to
apply ARTIFICIAL INTELLIGENCE

22. Individual Health Virtual Assistant:
 The overwhelming majority are approaching a
mobile phone in the present age. Propelled AI
equations, such as Cortana, Google Assistant, and
Siri, influence colleagues. They can offer
tremendous benefits to consumers at the moment
when combined with healthcare applications.
 As an individual well-being associate, medicinal
services applications will go around. They will also
be used to provide drug warnings, and it will also be
conceivable for human-like cooperation.
 Artificial intelligence will also help to assist patients
when the clinical faculty is not available as an
Similar advance approaches to
apply ARTIFICIAL INTELLIGENCE

23. Artificial intelligence improves “The Quality of
Sleep”:
 AI innovation is constantly trying to improve the
general nature of rest, from AI controlled savvy
bedding to infant screens, rest applications,
 Two bedtime songs were made by a medical
services organization named AXA PPP, one by AI
and the other by individuals.
 The AI framework could figure out mood and
concordance with profound learning to bring about
another organization.
 This piece was then transformed with the help of a
Similar advance approaches to
apply ARTIFICIAL INTELLIGENCE

24. Medicinal Imaging Analysis:
 Radiology is another significant field in medicinal
services that utilizes AI. With analytical procedures,
AI frameworks can help. It can look at therapeutic
images such as X-beams, CT reviews, MRIs, and
so on and can criticize what a human eye can skip.
 Therapeutic imaging research, along these lines,
turns out to be significantly more accurate and
viable. The odds of blunders are decreased.
 IBM Watson is a model for a livelihood. It can
provide clinicians with proof-based treatment
options for cancer patients in the field of oncology,
Similar advance approaches to
apply ARTIFICIAL INTELLIGENCE

25. Precision Medicine:
 Genomics is the part of atomic science that
manages genome structure, growth, ability, and
mapping. It searches for the connections from the
data obtained from the DNA to the disease.
 It is conceivable to detect cancer and some vascular
infections at the moment when combined with AI at
an all-around starting period.
 It can also predict the medical issues that patients
may face, depending on their characteristics.
 AI also assists in the machining of medicinal parts in
this way, minimizing the chances of operation
malfunction.
Similar advance approaches to
apply ARTIFICIAL INTELLIGENCE

26. Healthcare Bots:
 Innovation in artificial intelligence is also making
inroads in the customer service space.
 Patients can almost probably interact through a
talk window or by phone with these AI bots on the
web. Social insurance bots can be used to
arrange meetings with the provider of medicinal
services for the patient.
 Bots may also support patients with their
medication by providing 24 x 7 assistance, they
will likewise boost customer service.
Similar advance approaches to
apply ARTIFICIAL INTELLIGENCE

27. References:
 Murali N., Sivakumaran N., “Artificial Intelligence in
Healthcare – A Review.” , International Journal of
Modern Computation, Information and Communication
Technology, 2018,2581-5954.
 Pichika N., Mak K., Rao M.,“ Artificial intelligence in drug
development: present status and future prospects”,
Drug Discovery Today, Elsevier, 2018, 251-269.
 Kun-Hsing Yu, Andrew L., Kohane I. ,”Artificial
intelligence in healthcare. USA”, Journal of Biomedical
Engineering, 2018, 2, 8-27.
 Hosny A., Parmar C., Quackenbush J., Lawrence H.
Aerts L “Artificial intelligence in radiology”. Nature
Reviews Cancer, 2018, 1-19.
 Johnson K., Torres J., Glicksberg B., Shameer K.,
Riccardo Miotto R.,“Artificial Intelligence in Cardiology”,
International Journal of Communation technology,
2018, 7 1 , 0735-1097.

28.  Ganapathy K., Shabbir A., Nursetyo A., “Artificial
intelligence in neurosciences: A clinician's perspective”,
2018, 66-79.
 Matthew B., Maryellen L., Nicolai J. , Alireza M. , Allison
T., Raymond H. Mak , Rulla M.,“Artificial intelligence in
cancer imaging: Clinical challenges and applications”, A
Cancer Journal for Clinicians, 2019, 54-91.
 Baxter S., Xu J., Zhou X., He J., Zhang K.,“The practical
implementation of artificial intelligence technologies in
medicine.”, Journal of Nature Medicine , 2019, 25-80.
 Mahmoud A. and Obaidellah M., “Artificial Intelligence
Techniques for Cancer Detection and Classification:
Review Study.”, European Scientific Journal, 2017, 13,
1857 – 7881.
 Mishra S., Takke A., Auti S., Suryavanshi S. and Oza M.,
“Role of Artificial Intelligence in Health Care.“,
BioChemistry: An Indian Journal, 2017, 11, 0974-7427.
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References:

30. Thank You