台灣人工智慧學校陳昇瑋執行長 主題演講 : 邁向智慧醫療新時代 日期: 2019/08/17 09:00 - 12:30 (六) 地點: 高雄長庚醫院醫學大樓 6F 大禮堂

3. 10

4. 11

5. 16

6. (Polanyi's Paradox)
18
1964 /
/

9. Keyword in AI Research
21

10. 23

11. 24
Training a prediction machine by
showing examples instead of
programming it.
-Yann LeCun
(prediction machine: /- )

12. -/ 25
Find the common patterns
from the left waveforms
It seems impossible to
write a program for
speech recognition
You quickly get lost in the
exceptions and special cases.
(Slide Credit: Hung-Yi Lee)

13. A -
You said
“ ”
/I
I
(Slide Credit: Hung-Yi Lee)

14. -/ 28

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16. 33

17. 34

18. 35

19. 36

20. 37

21. 38

22. AI
40

23. 41

24. -
42

25. -
43

26. -
44

27. -
45

28. Deep Learning, Machine Learning, and AI
47

29. Healthy Diseased
Hemorrhages
No DR Mild DR Moderate DR Severe DR Proliferative DR
1 2 3 4 5

30. 51
Classical Machine Learning
Deep Learning
Rule-based System
Rule extraction
LessDomainExperts,
MoreEngineers&MoreAccuracy

31. 53

32. 54
Using
Deep Learning

33. There is no free lunch
55
Classical
Machine Learning
Deep Learning

34. 56https://www.kaggle.com/c/two-sigma-financial-news/leaderboard

35. 62(Slide Credit: McKinsey&Company)

36. AI
IN MEDICINE
68

37. 71

38. 0.95
F-score
Algorithm Ophthalmologist
(median)
0.91
“The study by Gulshan and colleagues truly
represents the brave new world in
medicine.”
“Google just published this paper in JAMA
(impact factor 44.405) [...] It actually lives
up to the hype.”
Dr. Andrew Beam, Dr. Isaac
Kohane Harvard Medical School
Dr. Luke Oakden-Rayner
University of Adelaide

39. Deep Learning for Detection of Diabetic Eye Disease
73
Algorithm’s F1-score: 0.95
Median F1-score of 8 ophthalmologists : 0.91

40. 74

41. AI
75 during 2008 – 2013 in New York City
, , , etc.
AI , 63% , 5%
AI vs. Judges
, (!)
, /
( , 1000 )
75

42. 76
https://www.youtube.com/watch?v=ljBOzdKuX7A

43. 77

44. 78

45. 79OCT: Optical CoherenceTomography ( )

46. 81

47. arxiv.org/abs/1703.02442
Tumor localization score (FROC):
model: 0.89
pathologist: 0.73
(Slide Credit: Google Brain)

48. 88
Deep Learning for Kidney Function Classification and
Prediction using Ultrasound-based Imaging
Chin-Chi Kuo1, Chun-Min Chang2, Kuan-Ting Liu2, Wei-Kai Lin2,
Chih-Wei Chung1, and Kuan-Ta Chen2
1
Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
2Institute of Information Science, Academia Sinica, Taiwan
eGFR
( )

49. 89

50. 10 XGBoost Models Ensemble
Accuracy 0.851
Precision 0.870
Recall 0.667
F1 score 0.757
AUC 0.91

51. Cardiologist-Level Arrhythmia Detection with
Convolutional Neural Networks
95
Goal: diagnose irregular heart rhythms, also known as
arrhythmias, from single-lead ECG signals better than a
cardiologist

52. Input and Output
Input: a time-series of raw ECG signal
The 30 second long ECG signal is sampled at 200 Hz
From 29,163 patients
Output: a sequence of rhythm classes
The model outputs a new prediction once every second
Total 14 rhythm classes are identified
96

53. 97

54. Model
34 layers NN
16 residual blocks
2 conv layers per block
Filter length = 16 samples
# filter = 64*k, k start from 1 and is
incremented every 4-th residual block
Every residual block subsamples
its input by a factor of 2
98

55. Results – F1 score
99

56. 101

57. 102

58. 103
* 28 out of 142 patients were labeled as depressed.https://www.csail.mit.edu/news/model-can-more-naturally-detect-depression-conversations

59. Synthetic Speech Generated from Brain Recordings
104https://www.ucsf.edu/news/2019/04/414296/synthetic-speech-generated-brain-recordings

60. Synthetic Speech Generated from Brain Recordings
105
https://www.sciencemag.org/news/2019/01/artificial-intelligence-turns-brain-activity-speech

61. Google Healthcare Focuses
106

62. Predictive tasks for healthcare
Given a large corpus of training data of de-identified medical records, can we
predict interesting aspects of the future for a patient not in the training set?
● will patient be readmitted to hospital in next N days?
● what is the likely length of hospital stay for patient checking in?
● what are the most likely diagnoses for the patient right now? and
why?
● what medications should a doctor consider prescribing?
● what tests should be considered for this patient?
● which patients are at highest risk for X in next month?
Collaborating with several healthcare organizations, including UCSF,
Stanford, and Univ. of Chicago. Have early promising results.

63. 108

64. DeepVariant: Creating a universal SNP and small indel variant caller with
deep neural networks
110

65. 111

66. DeepVariant vs. GATK
112

67. 113
DeepVariant vs. GATK

68. 115https://www.hbrtaiwan.com/article_content_AR0008072.html

69. 117
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. "Machine learning in
medicine." New England Journal of Medicine380.14 (2019): 1347-1358.

70. The 2014 HSBC Expat Explorer survey rates healthcare
118
https://expathealth.org/healthcare/top-5-health-care-expats/

71. LIMITATIONS OF AI
119

72. (Moravec’s Paradox)
High cognitive processes
Conscious processes
Chesses, math, problem
solving
Difficult for humans
Easy for computers
Low Cognitive processes
Perception, action,
fight/flight responses,
social interactions
Easy for humans
Difficult for computers
120

73. 125

74. 126

75. https://ifaketextmessage.com/

76. 130

77. Strong AI Weak AI
Can think
Own conscious
Act as it can think
Consciousless
(1980)

78. What we can and cannot today
What we can have
Safer car, autonomous car
Better medical image analysis
Personalized medicine
Adequate language translation
Useful but stupid chatbots
Information search, retrieval,
filtering
Numerous applications in
energy, finance, manufacturing,
commerce, law, …
What we cannot have (yet)
Machine with common sense
Intelligent personal assistants
“Smart” chatbots
Household robots
Agile and dexterous robots
Artificial General Intelligence
(AGI)
132
(Credit:Yann LeCun)

79. 134
2015 207
31,000 3,700
90% Markram

80. 139

81. 141

82. Generating adversarial patches against YOLOv2
142
https://www.youtube.com/watch?feature=youtu.be
&v=MIbFvK2S9g8&app=desktop

83. AI Don’t Know What They are Talking About
144
https://www.facebook.com/playgroundenglish/videos/629372370729430/?hc_ref=ARQ
HCaS2GZ9jUgZermEupF5yerADq2X9F9P40OR3n70poUiCy7R0X3oHrGxyLSrWVdI

84. Change is the only constant.
- Heraclitus (535 BC - 475 BC)
(Slide Credit: Albert Chen)

85. 153
https://www.israel21c.org/food-expiration-dates-are-about-to-undergo-a-revolution/

86. 154

87. Precision Medicine
156

88. Rainforest Connection
157

89. Rainforest Connection
158

90. 159
https://www.youtube.com/watch?v=ljBOzdKuX7A

91. 199
https://www.youtube.com/watch?v=ljBOzdKuX7A

92. AI …
200
2000 2018