Artificial Intelligence in Healthcare Sector

In the realm of artificial intelligence, individuals are adjusting to using AIs in many different ways. The healthcare industry, which employs AI in several critical domains, continues to be one of the most significant industries in the world. Let's explore them in further detail!

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. D iagnosis & treatment recommendations P atient engagement & adherence A dministrative activities

9. D iagnosis & T reatment R ecommendations

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. I t’s very time-consuming to align all voxels in the first volume with those in the second. M atching voxels is even more computationally complex P articularly slow when analyzing scans from large populations. Neuroscientists analyzing variations in brain structures across hundreds of patients could potentially take hundreds of hours D ifferent machines Different spatial orientations Invention of an a lgorithm 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.  T heir 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 S how 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. C ombine the  historical data and medical intelligence for the discovery of new drugs NLP applications that can understand and classify clinical documentation. NLP systems can analyze unstructured clinical notes on patients, giving incredible insight into understanding quality, improving methods, and 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

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

27. Convolutional Neural Network (CNN) M ost well-known image recognition and classification algorithm

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

29. 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.

30. 3 Future of AI in Health Care

31. 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 S till D ependent on Us

32. 4 But, Ethical Issues Remain

33. Confidential Data D ata privacy breaches Information l eakage Algorithmic biases and lack of fairness

34. Safety in Transparency Issue Blackbox development Principles of Informed Consent Increase unemployment rates

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