Artificial intelligence in field of Health care and pharmaceutical industry

1. Presented by : Yogesh Pralhad Jadhao
M.Pharm 1st ( QAT)
Dr. D Y patil college of pharmacy akurdi, Pune.
Guided by:- Dr. Sonali Mahaparale

2. 1. Introductions
2. Artificial intelligence in field of pharmacy
3. Why AI in pharma is a good idea ?
4. Imagine a future with AI
5. Investment in artificial intelligence
6. Steps of use of artificial intelligence
7. Scope of future research
8. Challenges of AI to adoptions

3. 9. Current scenario
10. Artificial intelligence in health care system
11. Application of AI in health care system and in pharma
12. Recent artificial intelligence adoptions
13. Conclusion
14. References.

4.  According to Father of Artificial Intelligence(AI),John
McCarthy, it is ,“The science and engineering of making
intelligent machines“.
 Artificial intelligence refers to the ability of a computer or a
computer enabled robotics system to process information
and produce outcomes in a manner similar to the thought
process of human in learning , decision making and
solving problems.
 AI is a simulation of human intelligence process by computer.
 The goals of AI system is to develop system capable of
taking complex problems in ways similar to human logic and
reasoning.

5. Three main elements of AI
1. Massive amount of data.
2. Sophisticated algorithm.
3. High performance parallel processor.
Three steps
 Computer and program.
 The turning tests.
 The dormant conference.

6.  It is one of the top technologies shaping the future of pharmacy.
 pharma industries has been developing cure and treatment of
centuries. Traditionally the design and manufacturing of drugs
required several years, lengthy clinical trials and required a huge
cost.
 With the rise of 21st century technology has been changing.
 In future we will see completely different drug design,
manufacture and clinical trials.

7.  Pharmaceutical industry can accelerate innovation by using
technological advancements.
 The recent technological advancement that comes to mind would
be artificial advancement such as visual perception, speech
recognition, decision-making & translation between various
languages.
 An estimate by IBM ( international business management) shows
that entire healthcare domain has approx billions GB of data
available.

8.  With huge data available in this domain, AI can be of real help in
analyzing the data
 presenting results that would help out in decision making, saving
human effort, time, money & thus help save lives.
 Effective use of incomplete data sets,
 rapid analysis of data

9.  Shorter time to market,
 Development of new products,
 Improved customer response,
 Improved confidence and AI would have a low error rate compared
to humans, if coded properly.
 They would have incredible precision, accuracy, and speed.

10.  Artificial intelligence is able to design new drug
 Find new drug combinations
 Deliver clinical trials with less time duration.
 Drug are not tested on real humans or animals but on virtual
models that are engineered to mimic the physiology of organ.
 AI can help us to reduce the cost and time required for
Various types of works.

11.  Last few year, vital artificial intelligence will be in their respective
industries and over 70% of them thought it would be very
important. From the same group, only 11% of businesses have
not considered investing in AI technology
 Furthermore, according to Narrative Science, 61% of companies
investing in innovative strategies are using AI to identify
opportunities.
 For pharmaceutical businesses that thrive on innovation, this is
an important statistic to understand.

13. How AI is shaping the future of pharmaceutical
industry
 Artificial Intelligence and Machine Learning are powering
incredible changes across a huge range of industries.
 But in data and research dependent industries such as
pharmaceutical industries, they are having a unparalleled impact
from improving candidate selection process for clinical trials, to
accelerating new drug development.

14. 1. Improving patient recruitment
2. Optimizing trial design
3. Trial output optimization
4. New drug identification
5. Speed up the clinical trials and research

15. What are some challenges to AI adoption at larger
organization
Data challenge :- Quality and quantity of data
As for any machine learning model to work efficiently, a training data
set with a minimum of 2 to 3 years of historical data is critical. This is
the most critical challenge in large organization.
Skill challenge :- Getting the right resource and with the right
background is very challenging.

16.  Data privacy and security:-
1. Data privacy and security are of the highest importance for all
organization.
2. They constantly ensure all data privacy, security laws are
followed.
3. Also appropriate training is provided across our different
portfolios and adhered to by partners and complementary
workers.

17.  Accessing the huge Data is Hard.
 Different data types (genetic, health records, image data, etc) often
exist in “silos”, and cleaning / accessing this data is often very
challenging in healthcare.
 Any developments that help to make data accessible and useable
for AI will be very meaningful in pharma

18.  The AI Talent Crunch is Real.
 Google, Amazon, and Facebook will often have the cash and appeal
to attract top AI talent.
 Pharma companies will probably need to retrain and “upgrade” the
skills of their existing data science / statistics teams

19.  Any big pharmaceutical companies being investing in AI in order
to develop Better diagnostics or biomarkers, to identify drug
target and to design new drugs and products
 Merck partnership with Numerate in March 2012 focusing on
generating novel small molecule drug leads for unnamed
cardiovascular disease target.
 In December, 2016 Pfizer and IBM announced partnership to
accelerate drug discovery in immuno-oncology.

21.  Disease identification
 Personalized treatment
 Drug Discovery and developments
 Clinical trials researches
 Manufacturing
 Epidemic outbreak predication
 Radiology and radiotherapy
 Smart electronic health records
 Regulating use of AI in digitals.
 Role of AI in pharmacovisulance

22.  AI can be implemented in almost every aspect of the
pharmaceutical industry, right from drug discovery and
development to manufacturing and marketing.
 By using and implementing AI systems in the core workflows,
pharma companies can make all business operations efficient,
cost-effective, and hassle-free.
 The best part is that since AI systems are designed to deliver
better outcomes as they continually learn from new data and
experience, they can be a powerful tool in the research and
development wing of the pharmaceutical industry.

23.  Berg, an innovative US bio pharma company, is using AI to research
and develop diagnostics and therapeutics in the fields of oncology,
endocrinology, and neurology
 Their unique AI-based Interrogative Biology platform combines
patient biology and AI-based analytics to identify differences
between healthy and disease environments.

24. Deep Mind's AI can detect
over 50 eye diseases as
accurately as a doctor
The system analyzes 3D
scans of the retina and could
help speed up diagnoses in
hospitals

25. Micro biosensors and devices, mobile apps :-
with more sophisticated health-measurement and remote
monitoring capabilities; these data can further be used for R&D.

26.  DermCheck:- app available in Google play store in which images
are sent to dermatologists.
 DermCheck app:- allows Smartphone users to describe their
symptoms & take photos of their skin concerns for review by a
board-certified dermatologist from the comfort of their own home.
Patients get prescriptions sent directly to their pharmacy without
ever stepping a foot into the dermatologist's office.

28.  From initial screening of drug compounds to predicted success rate
based on biological factors.
 R&D discovery technology; next-generation sequencing.
 Previous experiments are used to train the model
 Optimization software's (example: Form Rules)
 Designing of the processes
 AI can recognize hit and lead compounds, and provide a quicker
validation of the drug target and optimization of the drug structure
design

29. AI can be used effectively in different parts of drug discovery,
including drug design, chemical synthesis, drug screening,
polypharmacology, and drug repurposing

30.  A study published by the Massachusetts Institute of
Technology (MIT) has found that only 13.8% of drugs
successfully pass clinical trials.
 Furthermore, a company can expect to pay between $161
million to $2 billion for any drug to complete the entire clinical
trials process and get FDA approval.
 With this in mind, pharma businesses are using AI to increase the
success rates of new drugs while decreasing operational costs at
the same time.
 Ideally, this would also translate to lower drug costs for patients,
all while offering them more treatment choices.

31.  Artificial Intelligence can also determine the optimal sample sizes for
increased efficiency and reduce data errors such as duplicate
entries.
 Machine learning- to shape, direct clinical trials
 Advanced predictive analytics can analyze genetic information to
identify the appropriate patient population for a trial.
 Remote monitoring and real time data access for increased safety;
biological and other signals for any sign of harm or death to
participants

32.  Cutting costs
 Improving trial quality
 Improving trial time by almost half
 Finding biomarkers and gene signatures that cause diseases
 Recruiting trial patients in minutes
 Reading volumes of text and data in seconds

34. Pharma companies can implement AI in the manufacturing process for
higher productivity, improved efficiency, and faster production of life-
saving drugs. AI can be used to manage and improve all aspects of the
manufacturing process, including:
 Quality control
 Predictive maintenance
 Waste reduction
 Design optimization
 Process automation

35.  A good example of this AI application is the ML-based Malaria
Outbreak Prediction Model that functions as a warning tool
predicting any possible malaria outbreak and aid healthcare
providers in taking the best course of action to combat it.
 To predict malaria outbreaks, from data like temperature, average
monthly rainfall, total number of positive cases, etc.
 ProMED-mail is a internet based reporting program for monitoring
emerging diseases and providing outbreak reports.

36.  Google’s DeepMind Health is working with University College
London Hospital (UCLH) to develop machine learning algorithms
capable of detecting differences in healthy and cancerous tissues
 As radiation oncology moves toward use of magnetic
resonance imaging (MRI) radiotherapy treatment systems
because of the better soft tissue delineation and ability to provide
real-time imaging during treatment, AI will play a role to help
eliminate the need for CT scans.

37.  AI to help diagnosis, clinical
decisions, and personalized
treatment suggestions.
 Handwriting recognition and
transforming cursive or other
sketched handwriting into
digitized characters

39.  Novartis uses AI to predict untested components researchers
should explore to find new cures.
 IBM Watson helps match patients with the right drug trials.
 Verge Genomics uses AI to predict the effect of new treatments
for patients suffering from ALS & Alzheimer’s.
 Bayer and Merck & Co uses AI algorithms to identify pulmonary
hypertension.

40.  Tencent Holdings used AI to remotely monitor patients with
Parkinson’s.
 Mission Therapeutics uses AI to develop treatments for
Alzheimer’s.
 Healx uses AI to help biotech companies find treatments for rare
diseases.
 AiCure & AbbVie use image recognition to improve drug
adherence.
 Santen and twoXAR are using AI to develop drugs for glaucoma.

41.  AstraZeneca and Alibaba build AI to help patients with automated
cancer diagnostics.
 Apple uses AI to screen children for autism.
 GNS Healthcare and Genentech use AI to develop new cancer
therapies.
 Deep 6 uses AI to proactively find drug trial candidates

43. 1. AI is doubtless the next big thing for pharma Companies that are
more flexible and adopt AI faster will likely gain a strategic
advantage.
2. In fact, experts anticipate that implementing AI will soon be
necessary to compete in the industry.
3. However, the transformation will not happen overnight. Instead, it
will gradually occur over the next 10 or 20 years. The
4. AI is expected to be integrated into most, if not all, pharma R&D
operations. In turn, this should theoretically improve the drug
development success rate and streamline R&D efforts

44. 1. JA Dimasi, RW Hansen; The price of innovation: new estimates of
drug development costs. J Health Econ; 2003;22(2);151-185
2. Kit-kay and malikarjuna Rao Pichika; artificial intelligence in drug
development: present reference status and future prospectus;
drug discovery today’s; vol.24; 2019;(3);773-779.
3. Debleena Paul, Gaurav sanap; artificial intelligence in drug
discovery and development: drug discovery today; 2021jan
,26(1);80-93.
4. Kathleen walah, cognitive world; the increasing use of AI in the
pharmaceutical industry; frobe; 2020

45.  A review article of Artificial intelligence and pharma what next
? ; Codrin Arsence; Health care: Aug 4,2020; digital aurtor
parameter
 http://www.slideshare.net/swastik.jyoti/artificial intelligence-
ppt147888606 ? from –M-app.android.
 http://www.slideshare.net/atuladhikari/artificial intelligence
(in health care and pharmcy)PPT from m.android2018.
 http://www.youtube.com/themedicalfuturerist

46. Thank you
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