The document discusses the convergence of human and artificial intelligence in medicine. It outlines two major trends: 1) rising healthcare expenditures with no productivity growth, and 2) the generation of massive amounts of medical data that exceeds human abilities to analyze. While the integration of human and AI has barely begun, AI has the potential to solve problems in healthcare like diagnostic errors. The paper aims to summarize existing evidence for using AI in various medical fields like radiology, pathology, dermatology, ophthalmology, and cardiology. It provides examples of studies applying AI to tasks like detecting diseases in medical images and reports performance that matches or exceeds human experts. However, limitations and challenges for clinical adoption are also noted.

1. Dongmin Choi
High-performance medicine
: the convergence of human and artificial intelligence

2. Introduction
Two Major Trends of Medicine

3. Introduction
Two Major Trends of Medicine
1. Failed Business Model
: Expenditures to healthcare ↑, but No productivity growth

4. Introduction
Two Major Trends of Medicine
1. Failed Business Model
: Expenditures to healthcare ↑, but No productivity growth
2. The Generation of Data in Massive Quantities
: Exceeded Human Ability → Need Algorithms to do analytics

5. Introduction
Two Major Trends of Medicine
1. Failed Business Model
: Expenditures to healthcare ↑, but No productivity growth
2. The Generation of Data in Massive Quantities
: Exceeded Human Ability → Need Algorithms to do analytics
But "the Integration of Human & AI" Has Barely Begun

6. Introduction
Two Major Trends of Medicine
1. Failed Business Model
: Expenditures to healthcare ↑, but No productivity growth
2. The Generation of Data in Massive Quantities
: Exceeded Human Ability → Need Algorithms to do analytics
But "the Integration of Human & AI" Has Barely Begun
And AI might solve problems in healthcare (e.g – diagnostic errors, mistaking in treatment …)

7. Introduction
Two Major Trends of Medicine
1. Failed Business Model
: Expenditures to healthcare ↑, but No productivity growth
2. The Generation of Data in Massive Quantities
: Exceeded Human Ability → Need Algorithms to do analytics
But "the Integration of Human & AI" Has Barely Begun
And AI might solve problems in healthcare (e.g – diagnostic errors, mistaking in treatment …)
→ This Paper Summarized Existing Base of Evidence for the Use of AI in Medicine

8. Introduction
Artificial Intelligence

9. Introduction
Artificial Intelligence
Clinicians

10. Introduction
Artificial Intelligence
Clinicians Health System

11. Introduction
Artificial Intelligence
Clinicians Health System Patient

12. Artificial Intelligence for Clinicians

13. AI for Clinicians
- Radiology (영상의학과)
- Pathology (병리학과)
- Dermatology (피부과)
- Ophthalmology (안과)
- Cardiology (심장학)
- Gastroenterology (위장학)
- Mental Heath (정신과)
Almost every type of clinician will be using AI technology in the future

14. AI for Clinicians - Radiology (영상의학과)

15. AI for Clinicians - Radiology (영상의학과)
Screening Acute Neurologic Events
from head CT 3D scans
Detecting Diseases
in Chest X-ray Images

16. AI for Clinicians - Radiology (영상의학과)
Detecting Diseases
in Chest X-ray Images
NIH Chest X-rays Dataset
- 112,120 frontal-view Chest X-ray Images
- 14 diseases (Pneumonia, Pneumothorax, etc.)
- Open Dataset (https://www.kaggle.com/nih-chest-xrays/data)

17. AI for Clinicians - Radiology (영상의학과)
Detecting Diseases
in Chest X-ray Images
Study (1) : CheXNet
- 121 layer Convolutional Neural Network
- Pneumonia (폐렴) detection
- Compared with 4 Radiologists and outperformed them
- AUC : 0.76 (far from optimal)
https://stanfordmlgroup.github.io/projects/chexnet/

18. AI for Clinicians - Radiology (영상의학과)
Detecting Diseases
in Chest X-ray Images
Study (2) : 14 Different Diagnoses
- ResNet + Patch Slicing
- Make 14 different diagnoses
- AUC : 0.63 (Pneumonia) ~ 0.87 (Heart Enlargement)
https://arxiv.org/abs/1711.06373

19. AI for Clinicians - Radiology (영상의학과)
Detecting Diseases
in Chest X-ray Images
Study (3) : qure.ai
- DL Algorithm installed in Hospitals in India
- Interprets four chest X-ray key findings
- Accurate as 4 Radiologists
- AUC : 0.837 ~ 0.929 (Radiologist : 0.693 ~ 0.923)
http://qure.ai/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204155

20. AI for Clinicians - Radiology (영상의학과)
Detecting Diseases
in Chest X-ray Images
Study (4) : Cancerous Pulmonary Nodule Detection
- SNUH & Lunit
- Tested on 1 Internal Valid Set & 4 External Valid Set
- Outperformed 17 of 18 radiologists
https://doi.org/10.1148/radiol.2018180237

21. AI for Clinicians - Radiology (영상의학과)
Detecting Diseases
in X-ray Images
Study (5) : Wrist Fracture Diagnosis
- Fracture is frequent in emergency room
- Sensitivity : Unaided - 81% ↔ Aided - 92%
https://www.ncbi.nlm.nih.gov/pubmed/30348771

22. AI for Clinicians - Radiology (영상의학과)
Screening Acute Neurologic Events
from head CT 3D scans
A study focusing on the breadth of
acute neurologic events
- Neurologic events, such as stroke or head trauma
- Over 37,000 head CT 3D scans
- Analyzed for 13 different anatomical findings
- AUC : 0.73
- Interpretation time : 150x faster (1.2 sec)
- Limitation : accuracy was poorer than human
https://www.ncbi.nlm.nih.gov/pubmed/30104767

23. AI for Clinicians - Radiology (영상의학과)
Limitations
- Dataset : A Large Number of Labeled Scans are Required
- Incomparability : cannot compare two DNN because of different methodology
- Imperfect metrics : ROC and AUC metrics are not necessarily indicative of clinical utility

24. AI for Clinicians - Radiology (영상의학과)
Limitations
- Dataset : A Large Number of Labeled Scans are Required
- Incomparability : cannot compare two DNN because of different methodology
- Imperfect metrics : ROC and AUC metrics are not necessarily indicative of clinical utility
< AI Chasm >
Hight AUC Performance
Algorithm
Clinical Efficacy

25. AI for Clinicians - Pathology (병리학과)
Pathology
- The Study of The Causes and Effects of Disease or Injury
- Much Slower at Adapting Digitization of Scans than Radiology
- Marked Heterogeneity and Inconsistency Among Pathologists’ Interpretations

26. AI for Clinicians - Pathology (병리학과)
- Whole-Slide Images
- 270 Training Dataset + 130 Test Dataset
- https://camelyon16.grand-challenge.org/Data/

27. AI for Clinicians - Pathology (병리학과)
- CAMELYON 2016 Summary Paper
- Among 32 algorithms, 5 DL algorithms outperformed 11 pathologists
- Two Settings
1. With Time Constraint (1min / slide) : Best Pathologist (0.810) ↔ Best Algorithm (0.994)
2. Without Time Constraint : Pathologist (0.966) ↔ Top 5 Algorithms (0.960)
https://jamanetwork.com/journals/jama/article-abstract/2665774

28. AI for Clinicians - Pathology (병리학과)
ML using tumor DNA
methylation patterns via sequencing
Pathologists
using traditional histological data
DNA methylation-based classification of central nervous system tumours
http://discovery.ucl.ac.uk/10043462/1/Brandner_DNA methylation-based classification of central nervous system tumours.pdf

29. AI for Clinicians - Pathology (병리학과)
ML using tumor DNA
methylation patterns via sequencing
Pathologists
using traditional histological data
DNA methylation-based classification of central nervous system tumours
→ DNA Methylation Generates Extensive data But Rarely Used in Clinic for Classification of Tumors
→ This Study Suggests Another Potential for AI
http://discovery.ucl.ac.uk/10043462/1/Brandner_DNA methylation-based classification of central nervous system tumours.pdf

30. AI for Clinicians - Pathology (병리학과)
- Focused on the Synergy of the Combined Pathologist & Algorithm
- Assisted by Algorithm, Pathologist achieved better performance and
shorter review time.
https://insights.ovid.com/pubmed?pmid=30312179

31. AI for Clinicians - Pathology (병리학과)
- The Use of a DL to Sharpen Out-of-focus Images
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2087-4

32. AI for Clinicians - Dermatology (피부과)
- Nearly 130,000 photographic and dermascopic digitized images
- AUC : 0.96 for Carcinoma & 0.94 for Melanoma
- Similar Performance Compared with 21 US board-certified Dermatologists
https://www.nature.com/articles/nature21056

33. AI for Clinicians - Dermatology (피부과)
< AUC for Melanoma >
CNN (0.86)
58 International Dermatologists (0.79)
>
- 12 Skin Disease Diagnosis
- AUC for Melanoma 0.96
https://www.jidonline.org/article/S0022-202X(18)30111-8/abstract

34. AI for Clinicians - Dermatology (피부과)
Limitations
- None of these studies were conducted in the clinical setting (physical inspection, responsibility, etc.)

35. AI for Clinicians - Ophthalmology (안과)

36. AI for Clinicians - Ophthalmology (안과)
Artificial Intelligence With Deep Learning Technology
Looks Into Diabetic Retinopathy Screening
- Over 128,000 Training Dataset
- Over 10,000 Test Dataset
- AUC : 0.99
https://jamanetwork.com/journals/jama/article-abstract/2588762

37. AI for Clinicians - Ophthalmology (안과)
Automated Grading of Age-Related Macular Degeneration
From Color Fundus Images Using Deep Convolutional Neural Networks
- Diagnosis of Age-related Macular Degeneration (AMD)
- Accuracy : 88% ~ 92% (as high as expert ophthalmologists)
https://www.ncbi.nlm.nih.gov/pubmed/28973096

38. AI for Clinicians - Ophthalmology (안과)
DL algorithm for Interpreting Retinal OCT
for Diagnosis of Diabetic Retinopathy and AMD
- Over 100,000 Training Dataset
- Over 1,000 Test Dataset
- AUC : 0.999
https://iovs.arvojournals.org/article.aspx?articleid=2727191

39. AI for Clinicians - Ophthalmology (안과)
- Developed an deep-learning OCT algorithm for urgent referral
-
- Compared with 4 Retina Specialist and 4 Optometrist (검안사)
- Conducted on Clinical Setting (OCT + Fundus Image + Clinical Note)
https://www.nature.com/articles/s41591-018-0107-6

40. AI for Clinicians - Ophthalmology (안과)

41. AI for Clinicians - Ophthalmology (안과)
- AI-based Device To Detect Certain Diabetes-Related
Eyes Problems
- FDA approved
- 87% Sensitivity & 91% Specificity for 819 patients
- Autonomous detection (w/o the need for a clinician)
https://www.eyediagnosis.net/

42. AI for Clinicians - Ophthalmology (안과)
Association of Retinal Neurodegeneration on Optical Coherence Tomography
With Dementia: A Population-Based Study
Dementia (치매)
Retinal OCT
https://jamanetwork.com/journals/jamaneurology/fullarticle/10.1001/jamaneurol.2018.1563

43. AI for Clinicians - Ophthalmology (안과)
- Retinal fundus images alone can be used to predict multiple cardiovascular(심혈관질환) risk factors
such as age, gender, and SBP (Systolic Blood Pressure)
- Notably, AUC for gender is 0.97
https://www.nature.com/articles/s41551-018-0195-0

44. AI for Clinicians – Cardiology (심장학)

45. AI for Clinicians – Cardiology (심장학)
Electrocardiograms (ECG, 심전도) Echocardiography (심장초음파검사)
The Major Images that Cardiologists Use in Practice

46. AI for Clinicians – Cardiology (심장학)
Detecting and interpreting myocardial infarction using fully convolutional neural networks
PTB ECG Dataset
https://physionet.org/physiobank/database/ptbdb/
- A Small Open Dataset : 549 ECGs
- Diagnose Heart Attack
- Performance : Sensitivity 93% & Specificity 90%
(Comparable with Cardiologists)
https://arxiv.org/pdf/1806.07385

47. AI for Clinicians – Cardiology (심장학)
- A Large Dataset : 64,121 ECGs from 29,163 patients
- Diagnose irregular hear rhythms, arrhythmias
https://stanfordmlgroup.github.io/projects/ecg/

48. AI for Clinicians – Cardiology (심장학)
- 8,666 Echocardiograms
- Classification of HCM (비후성 심근증),
Cardiac Amyloid (아밀로이드증),
PAH (폐동맥 고혈압)
https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.034338

49. AI for Clinicians – Gastroenterology (위장학)

50. AI for Clinicians – Gastroenterology (위장학)
Diminutive polyp
Making Colonoscopy Smarter With Standardized Computer-Aided Diagnosis
- Tiny polyps detection
- 94% Accuracy & 96% Negative Predictive Value
- Test during real-time colonoscopy (대장내시경)
https://annals.org/aim/article-abstract/2697090/making-colonoscopy-smarter-
standardized-computer-aided-diagnosis?doi=10.7326%2fM18-1901

51. AI for Clinicians – Gastroenterology (위장학)
- In Real-time Video Analysis, 25 frames / sec (Multi-threaded, NVIDIA Titan X pascal GPU)
https://www.nature.com/articles/s41551-018-0301-3

52. AI for Clinicians – Mental Health (정신과)

53. AI for Clinicians – Mental Health (정신과)
Digital Tools
Keyboard Interaction
Facebook Posts Interactivate Chatbots
Depression Diagnosis
Pred

54. AI for Clinicians
Examples of the breadth of AI applications across human lifespan

55. Artificial Intelligence for Health Systems

56. AI for Health Systems
Being able to predict key outcomes
More Efficient & Precise
use of hospital palliative care resources

57. AI for Health Systems
Reinforcement Learning
- Reinforcement Learning recommends the use of vasopressor (저혈압 치료제),
intravenous fluids (정맥 주사액), and the dose of the treatment for patients with sepsis (패혈증)
- ‘AI Clinician’ was more effective than humans
https://www.nature.com/articles/s41591-018-0213-5

58. AI for Health Systems
- A company providing health systems with estimated of risk of
readmission and mortality based on EHR data
https://www.welcome.ai/careskore

59. AI for Health Systems
Other Applications
- FDA-approved wearable sensors monitoring all vital signs
- AI chatbot that provides exome sequencing
- Development of a massive data infrastructure to support nearest-neighbor analysis (digital twins)
https://www.hindawi.com/journals/ijae/2011/154798/

60. AI for Health Systems
Limitations of Studies
- Incompleteness of data input : unstructured data has not been incorporated
- Miss K-fold Cross-validation
- Debate about using AUC as the key performance metric because it ignores actual probability values

61. Artificial Intelligence for Patient

62. AI for Patients
Apple Watch Series 4
(FDA approved)
Monitor medical adherence
Image recognition of food
for calorie and nutritional content

63. Limitations

64. Limitations
1. Black box of algorithms
1. Inequity : Lack of inclusion of minorities in datasets

66. Thank you