Table 3.
Oncologists’ characteristics in relation to their concerns about AIa (part 1).
| Characteristics | Total oncologists, N | AI misleads diagnosis and treatment | Overreliance on AI | Data and algorithm bias | ||||||||||||||||
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Oncologists, n (%)b | Chi-square (df) | P value | Oncologists, n (%) | Chi-square (df) | P value | Oncologists, n (%) | Chi-square (df) | P value | |||||||||
| Sex | 0.021 (1) | .88 |
|
0.326 (1) | .57 |
|
0.05 (1) | .82 | ||||||||||||
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Male | 135 | 97 (71.8) |
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94 (69.6) |
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|
72 (53.3) |
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|
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Female | 93 | 66 (71) |
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68 (73.1) |
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51 (54.8) |
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|
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| Education degree | 1.827 (2) | .40 |
|
1.665 (2) | .44 |
|
6.746 (2) | .03c | ||||||||||||
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Bachelor’s | 49 | 36 (73.5) |
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38 (77.6) |
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34 (69.4) |
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|
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Master’s | 126 | 93 (73.8) |
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89 (70.6) |
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60 (47.6) |
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Doctoral | 53 | 34 (64.2) |
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35 (66) |
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29 (54.7) |
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|
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| Years of clinical practice | 5.187 (2) | .08 |
|
3.556 (2) | .17 |
|
2.634 (2) | .27 | ||||||||||||
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|
0-10 | 89 | 67 (75.3) |
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57 (64) |
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|
50 (56.2) |
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|
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11-20 | 83 | 52 (62.6) |
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62 (74.7) |
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48 (57.8) |
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≥21 | 56 | 44 (78.6) |
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43 (76.8) |
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25 (44.6) |
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|
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| Specialty | 1.769 (3) | .62 |
|
1.242 (3) | .74 |
|
3.6 (3) | .31 | ||||||||||||
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Medical oncology | 97 | 67 (69.1) |
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66 (68) |
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|
56 (57.7) |
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|
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Surgical oncology | 77 | 54 (70.1) |
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55 (71.4) |
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|
35 (45.4) |
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Radiation therapy | 40 | 32 (80) |
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31 (77.5) |
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23 (57.5) |
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Others | 14 | 10 (71.4) |
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10 (71.4) |
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|
9 (64.3) |
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|
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| Hospital type | 0.563 (1) | .90 |
|
0.756 (1) | .38 |
|
1.34 (1) | .25 | ||||||||||||
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University hospital | 148 | 41 (76.5) |
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108 (73) |
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84 (56.8) |
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Nonuniversity hospital | 80 | 56 (69.7) |
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54 (67.5) |
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39 (48.8) |
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|
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| IT experience | 0.158 (1) | .69 |
|
5.651 (1) | .02c |
|
1.321 (1) | .25 | ||||||||||||
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Yes | 35 | 26 (74.3) |
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19 (54.3) |
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22 (62.9) |
|
|
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No | 193 | 137 (71) |
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|
143 (74.1) |
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|
101 (52.3) |
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|
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aAI: artificial intelligence.
bPercentages expressed with the value in the “Total oncologists, N” column as the denominator.
cP<.05.