The document discusses the role of artificial intelligence (AI) in healthcare, highlighting its potential to enhance diagnostic accuracy, improve surgical outcomes, and streamline administrative tasks. It emphasizes the success of machine-learning algorithms, such as MIT's 'voxelmorph,' which drastically reduce the time required to analyze medical scans. However, it also addresses ethical concerns including data privacy, algorithmic bias, and the potential for increased unemployment in the sector.

1. Artificial
Intelligence
in Health
CSE 604: AI

2. TEAM AIMBOT
Tasmia
Zerin
BSSE 1128
Mustahid
Hasan
BSSE 1114
MD. Siam
BSSE 1104
Khalid Hasan
BSSE 1135

3. What is Artificial
Intelligence?
The development of computer system that are capable of
performing tasks. It normally requires human intelligence like
decision making, object detection, solving complex problems
and so on.

4. Why is it needed in
Health Care?

5. Complexity and rise of
data in Healthcare
Risk and Complexities
in critical surgeries
Less Success rate of
Detection of Diseases
Recent development
of effective algorithms
Problems
Advantages

6. The global artificial intelligence in healthcare market size was valued at USD 10.4
billion in 2021 is expected to expand at a compound annual growth rate (CAGR) of
38.4% from 2022 to 2030

7. Use cases of AI in
Health Care
1

8. Diagnosis &
treatment
recommendations
Patient
engagement &
adherence
Administrative
activities

9. Diagnosis &
Treatment
Recommendatio
ns

10. If a patient has a brain tumor, for instance, doctors can overlap a brain scan from several
months ago onto a more recent scan to analyze small changes in the tumor’s progress
This process, however, can often take
two hours or more
as traditional systems meticulously align each of potentially a million pixels
in the combined scans.

11. MIT researchers describe a machine-learning algorithm that can register brain
scans and other 3-D images more than
1,000 times more quickly
using novel learning techniques.

12. MRI
Scan
• Basically are hundreds of stacked 2-D images
• form massive 3-D images called “Volumes”
• containing a million or more 3-D pixels, called
“Voxels”

13. It’s very time-consuming to align all voxels in the first volume with those in the
second.
Matching voxels is even
more computationally
complex
Particularly slow when analyzing scans from large populations.
Neuroscientists analyzing variations in brain structures across hundreds of
patients could potentially take
hundreds of hours
Different
machines
Different
spatial
orientation
s
Invention of an algorithm that makes the process of comparing 3D scans up to 1,000 times
faster

14. The MIT researchers’ algorithm, called “VoxelMorph” is powered by a
convolutional neural network (CNN), an unsupervised machine-learning approach
commonly used for image processing.
Their algorithm could accurately register all of their 250 test brain scans
within two minutes using a traditional CPU, and in under one second using a GPU
currently running the algorithm on lung images, image scanning before or during some
surgeries

15. Decision Making
• AI based surgical robots (example:
Da Vinci Surgical Robot and AI)
• Minimize errors
• Increases the efficiency of
surgeon
Provides surgeons with
advanced instruments
Da Vinci Surgical
System
Translates the surgeons hand
movements at the console in real
time
Delivers highly magnified, 3D high
definition views in the surgical area

16. Patient
Engagement &
Adherence

17. Apple
Watch

18. • Monitors health of an individual
• Collect data
• Show potential to monitor ECG data in a non-clinical
setting
• Predicts the risk of a heart attack
• Uses ANN for predicting health condition

19. Virtual Medical
Assistance
Virtual nursing assistants corresponds to the
maximum near-term value of $20B by 2027

20. Speech Recognition
Detects speech
Integrates NLP
Converts speech to text using
NLP and formats according to
medical report
Wireless integration
of medical device
Blood pressure
cuffs

21. Self Care Clinical Advice
Scheduling an
Appointment
Nurse Line
Corti - an AI tool that assists emergency medicine
staff

22. Administrative
Activities

23. • Combine the historical data and medical intelligence for the discovery of
new drugs
• NLP applications that can understand and classify clinical documentation.
NLP systems can
o analyze unstructured clinical notes on patients,
o giving incredible insight into understanding quality,
o improving methods, and
o better results for patients.

24. 2
Algorithms of AI in
Health Care

25. Artificial Neural Network
(ANN)
collection of connected units or nodes called artificial neurons,
which loosely model the neurons in a biological brain.
Back
Propagation

27. i1=radio frequency
i2=wave gredients
i3=magnetic fields

28. Convolutional Neural Network
(CNN)
Most well-known image recognition and classification algorithm

29. Using the technology in medical settings is controversial because of the
risk of accidental data release.
Many systems are owned and controlled by private companies, giving
them access to confidential patient data
-- and the responsibility for protecting it

30. The technique can be applied without the need for any data to be released to third party
companies or to be sent between hospitals or across international borders.
Swarm learning trains AI algorithms to detect patterns in data in a
local hospital or university, such as genetic changes within images of
human tissue.

31. 3
Future of AI in
Health Care

32. Helping, Not Replacing
The machine learning
programs will
automate, not
replace, human physicians
Physicians performance will
continuously
leverage to improve the
AI’s effectiveness
AI diagnostic assistant would be
an
invaluable partner
both as a training tool and a
safety measure
Closer to Us, But Still Dependent on Us

33. 4
But, Ethical Issues
Remain

34. • Confidential Data
o Data privacy breaches
o Information leakage
• Algorithmic biases and lack of fairness

35. • Safety in Transparency Issue
o Blackbox development
• Principles of Informed Consent
• Increase unemployment rates

36. THANK YOU