Abstract
This survey study describes the results of a nationally representative survey on artificial intelligence use in health care delivery.
Introduction
Applications of artificial intelligence (AI) in health care have increased in the past decade,1 but little is known about how patients view these applications and whether they have concerns.2 We conducted a nationally representative survey to understand public perceptions of the use of AI in diagnosis and treatment.
Methods
The survey was administered between December 3, 2019, and December 18, 2019, by an independent research firm using a hybrid probability-based, nationally representative online panel, which was weighted to correct for biases in sampling and nonresponse across demographic groups3 (eAppendix 1 in the Supplement). This survey study was deemed exempt from review by the institutional review boards at Yale University and Weill Cornell because identities were kept confidential. We followed the (AAPOR) reporting guideline.
Adjusted response rate (54%) was calculated using the RR3 formula of the American Association for Public Opinion Research.2,4 χ2 tests were used for statistical analysis across demographic characteristics, and significance was set at 2-sided P < .05.
Results
A total of 926 respondents (471 women [50.9%], 455 men [49.1%]) completed the survey (eAppendix 2 in the Supplement). Most patients believed that AI would make health care much better (10.9%) or somewhat better (44.5%), whereas some believed AI would make health care somewhat worse (4.3%) or much worse (1.9%); 19% indicated they did not know (Table 1).
Table 1. Public Views About Artificial Intelligence in Health Care.
| Response | Respondents, No. (%) | ||||||
|---|---|---|---|---|---|---|---|
| Total (n = 926) | Age | Race and ethnicitya | Answer to question 1b | ||||
| 18-49 y (n = 533) | ≥50 y (n = 392) | White (n = 551) | Other (n = 350)c | Don’t know (n = 176) | Other answer (n = 750) | ||
| Overall, in the next 5 y, do you think AI will make health care in the United States? | |||||||
| Much better | 101 (10.9) | 60 (11.3) | 41 (10.5) | 63 (11.4) | 35 (10.0) | NA | NA |
| Somewhat better | 412 (44.5) | 239 (44.8) | 173 (44.1) | 245 (44.5) | 161 (46.0) | NA | NA |
| Minimal change | 179 (19.3) | 105 (19.7) | 73 (18.6) | 110 (20.0) | 61 (17.4) | NA | NA |
| Somewhat worse | 40 (4.3) | 22 (4.1) | 18 (4.6) | 26 (4.7) | 12 (3.4) | NA | NA |
| Much worse | 18 (1.9) | 10 (1.9) | 8 (2.0) | 9 (1.6) | 7 (2.0) | NA | NA |
| Don’t know | 176 (19.0) | 97 (18.2) | 79 (20.2) | 98 (17.8) | 74 (21.1) | NA | NA |
| How important do you think it is that you are told when an AI program has played a big role in your diagnosis or treatment? | |||||||
| Not important | 39 (4.2) | 25 (4.7) | 14 (3.6) | 24 (4.4) | 13 (3.7) | 7 (4.0) | 32 (4.3) |
| Somewhat important | 276 (29.8) | 167 (31.3) | 109 (27.8) | 159 (28.9) | 110 (31.4) | 41 (23.3) | 235 (31.3) |
| Very important | 611 (66.0) | 341 (64.0) | 269 (68.6) | 368 (66.8) | 227 (64.9) | 128 (72.7) | 483 (64.4) |
| How important do you think it is that you are told when an AI program has played a small role in your diagnosis or treatment? | |||||||
| Not important | 125 (13.5) | 83 (15.6) | 42 (10.7) | 77 (14.0) | 45 (12.9) | 10 (5.7) | 115 (15.3) |
| Somewhat important | 379 (40.9) | 239 (44.8) | 140 (35.7) | 221 (40.1) | 149 (42.6) | 61 (34.7) | 318 (42.4) |
| Very important | 422 (45.6) | 211 (39.6) | 210 (53.6) | 253 (45.9) | 156 (44.6) | 105 (59.7) | 317 (42.3) |
| How comfortable would you be receiving a diagnosis from a computer program that made the right diagnosis 90% of the time but could not explain why it made the diagnosis? | |||||||
| Very uncomfortable | 287 (31.0) | 168 (31.5) | 118 (30.1) | 167 (30.3) | 110 (31.4) | 56 (31.8) | 231 (30.8) |
| Somewhat uncomfortable | 375 (40.5) | 207 (38.8) | 168 (42.9) | 231 (41.9) | 137 (39.1) | 72 (40.9) | 303 (40.4) |
| Somewhat comfortable | 204 (22.0) | 124 (23.3) | 80 (20.4) | 120 (21.8) | 78 (22.3) | 39 (22.2) | 165 (22.0) |
| Very comfortable | 60 (6.5) | 34 (6.4) | 26 (6.6) | 33 (6.0) | 25 (7.1) | 9 (5.1) | 51 (6.8) |
| How comfortable would you be receiving a diagnosis from a computer program that made the right diagnosis 98% of the time but could not explain why it made the diagnosis? | |||||||
| Very uncomfortable | 188 (20.3) | 113 (21.2) | 74 (18.9) | 104 (18.9) | 75 (21.4) | 47 (26.7) | 141 (18.8) |
| Somewhat uncomfortable | 349 (37.7) | 201 (37.7) | 148 (37.8) | 196 (35.6) | 144 (41.1) | 68 (38.6) | 281 (37.5) |
| Somewhat comfortable | 297 (32.1) | 170 (31.9) | 127 (32.4) | 190 (34.5) | 103 (29.4) | 49 (27.8) | 248 (33.1) |
| Very comfortable | 92 (9.9) | 49 (9.2) | 43 (11.0) | 61 (11.1) | 28 (8.0) | 12 (6.8) | 80 (10.7) |
Abbreviations: AI, artificial intelligence; NA, not applicable.
Race and ethnicity were self-identified.
Question 1: Overall, in the next 5 years, do you think AI will make health care in the United States?
Other (than White Hispanic and White non-Hispanic) race and ethnicity included Asian, Chinese, or Japanese; Black Hispanic; Black non-Hispanic; unspecified Hispanic; Native American, American Indian, or Alaska Native; Native Hawaiian and other Pacific Islander; other race; mixed race; and refused to answer.
Regarding being informed if AI played a big role in their diagnosis or treatment, 66% of respondents deemed it very important and 29.8% stated it was somewhat important. Thirty-one percent of respondents reported being very uncomfortable and 40.5% were somewhat uncomfortable with receiving a diagnosis from an AI algorithm that was accurate 90% of the time but incapable of explaining its rationale. Responses were similar by age and race and ethnicity. Compared with respondents who shared their views about the potential implications of AI for health care, more respondents who answered with “don’t know” deemed it very important to be told when AI played a small role in their diagnosis or treatment (59.7% vs 42.3%) and were very uncomfortable with receiving an AI diagnosis that was accurate 98% of the time but could not be explained (26.7% vs 18.8%) (Table 1).
Comfort with AI varied by clinical application (Table 2). For example, 12.3% of respondents were very comfortable and 42.7% were somewhat comfortable with AI reading chest radiographs, but only 6.0% were very comfortable and 25.2% were somewhat comfortable about AI making cancer diagnoses. Most respondents were very concerned or somewhat concerned about AI’s unintended consequences, including misdiagnosis (91.5%), privacy breaches (70.8%), less time with clinicians (69.6%), and higher health care costs (68.4%). A higher proportion of respondents who self-identified as being members of racial and ethnic minority groups indicated being very concerned about these issues, compared with White respondents.
Table 2. Public Comfort and Concerns With Artificial Intelligence in Health Care.
| Respondents, No. (%) | |||||
|---|---|---|---|---|---|
| Total (n = 926) | Age 18-49 y (n = 533) | Age ≥50 y (n = 392) | White racea (n = 551)b | Other race and ethnicity (n = 350)a,c | |
| How comfortable you would feel with AI doing some of the things your doctor usually does for each of the following | |||||
| Reading your chest x-ray | |||||
| Very uncomfortable | 159 (17.2) | 89 (16.7) | 70 (17.9) | 89 (16.2) | 64 (18.3) |
| Somewhat uncomfortable | 258 (27.9) | 142 (26.6) | 115 (29.3) | 162 (29.4) | 86 (24.6) |
| Somewhat comfortable | 395 (42.7) | 223 (41.8) | 172 (43.9) | 233 (42.3) | 154 (44.0) |
| Very comfortable | 114 (12.3) | 79 (14.8) | 35 (8.9) | 67 (12.2) | 46 (13.1) |
| Making the diagnosis of pneumonia | |||||
| Very uncomfortable | 180 (19.4) | 95 (17.8) | 84 (21.4) | 103 (18.7) | 69 (19.7) |
| Somewhat uncomfortable | 302 (32.6) | 178 (33.4) | 124 (31.6) | 164 (29.8) | 130 (37.1) |
| Somewhat comfortable | 357 (38.6) | 205 (38.5) | 152 (38.8) | 230 (41.7) | 119 (34.0) |
| Very comfortable | 87 (9.4) | 55 (10.3) | 32 (8.2) | 54 (9.8) | 32 (9.1) |
| Telling you that you have pneumonia | |||||
| Very uncomfortable | 257 (27.8) | 148 (27.8) | 108 (27.6) | 138 (25.0) | 110 (31.4) |
| Somewhat uncomfortable | 325 (35.1) | 178 (33.4) | 147 (37.5) | 203 (36.8) | 113 (32.3) |
| Somewhat comfortable | 258 (27.9) | 143 (26.8) | 115 (29.3) | 152 (27.6) | 101 (28.9) |
| Very comfortable | 85 (9.2) | 64 (12.0) | 21 (5.4) | 57 (10.3) | 26 (7.4) |
| Recommending the type of antibiotics you get | |||||
| Very uncomfortable | 192 (20.7) | 103 (19.3) | 89 (22.7) | 107 (19.4) | 76 (21.7) |
| Somewhat uncomfortable | 248 (26.8) | 130 (24.4) | 117 (29.8) | 144 (26.1) | 97 (27.7) |
| Somewhat comfortable | 359 (38.8) | 215 (40.3) | 144 (36.7) | 217 (39.4) | 134 (38.3) |
| Very comfortable | 127 (13.7) | 85 (15.9) | 42 (10.7) | 83 (15.1) | 43 (12.3) |
| Making the diagnosis of cancer | |||||
| Very uncomfortable | 396 (42.8) | 216 (40.5) | 179 (45.7) | 225 (40.8) | 160 (45.7) |
| Somewhat uncomfortable | 241 (26.0) | 138 (25.9) | 103 (26.3) | 154 (27.9) | 80 (22.9) |
| Somewhat comfortable | 233 (25.2) | 138 (25.9) | 95 (24.2) | 139 (25.2) | 88 (25.1) |
| Very comfortable | 56 (6.0) | 41 (7.7) | 15 (3.8) | 33 (6.0) | 22 (6.3) |
| Telling you that you have cancer | |||||
| Very uncomfortable | 533 (57.6) | 297 (55.7) | 235 (59.9) | 322 (58.4) | 200 (57.1) |
| Somewhat uncomfortable | 224 (24.2) | 121 (22.7) | 103 (26.3) | 141 (25.6) | 74 (21.1) |
| Somewhat comfortable | 120 (13.0) | 78 (14.6) | 42 (10.7) | 59 (10.7) | 56 (16.0) |
| Very comfortable | 49 (5.3) | 37 (6.9) | 12 (3.1) | 29 (5.3) | 20 (5.7) |
| How concerned you are about the use of AI in medicine for each of the following | |||||
| My health information will not be kept confidential | |||||
| Not concerned | 270 (29.2) | 163 (30.6) | 107 (27.3) | 174 (31.6) | 91 (26.0) |
| Somewhat concerned | 359 (38.8) | 204 (38.3) | 155 (39.5) | 227 (41.2) | 124 (35.4) |
| Very concerned | 297 (32.1) | 166 (31.1) | 130 (33.2) | 150 (27.2) | 135 (38.6) |
| The AI will make the wrong diagnosis | |||||
| Not concerned | 79 (8.5) | 48 (9.0) | 31 (7.9) | 51 (9.3) | 25 (7.1) |
| Somewhat concerned | 477 (51.5) | 267 (50.1) | 209 (53.3) | 302 (54.8) | 161 (46.0) |
| Very concerned | 370 (40.0) | 218 (40.9) | 152 (38.8) | 198 (35.9) | 164 (46.9) |
| AI will mean I spend less time with my doctor | |||||
| Not concerned | 280 (30.2) | 178 (33.4) | 102 (26.0) | 171 (31.0) | 104 (29.7) |
| Somewhat concerned | 356 (38.4) | 184 (34.5) | 172 (43.9) | 219 (39.7) | 126 (36.0) |
| Very concerned | 289 (31.2) | 170 (31.9) | 118 (30.1) | 161 (29.2) | 119 (34.0) |
| AI will increase my health care costs | |||||
| Not concerned | 293 (31.6) | 181 (34.0) | 112 (28.6) | 195 (35.4) | 90 (25.7) |
| Somewhat concerned | 298 (32.2) | 155 (29.1) | 142 (36.2) | 189 (34.3) | 101 (28.9) |
| Very concerned | 335 (36.2) | 197 (37.0) | 138 (35.2) | 167 (30.3) | 159 (45.4) |
Abbreviation: AI, artificial intelligence.
Race and ethnicity were self-identified.
Other (than White Hispanic and White non-Hispanic) race and ethnicity included Asian, Chinese, or Japanese; Black Hispanic; Black non-Hispanic; unspecified Hispanic; Native American, American Indian, or Alaska Native; Native Hawaiian and other Pacific Islander; other race; mixed race; and refused to answer.
Discussion
Most respondents had positive views about AI’s ability to improve care but had concerns about its potential for misdiagnosis, privacy breaches, reducing time with clinicians, and increasing costs, with racial and ethnic minority groups expressing greater concern. Respondents were more comfortable with AI in specific clinical settings, and most wanted to know when AI was used in their care.
One limitation of this study was it involved a panel that had agreed to participate in surveys, which may limit generalizability. In addition, compared with nonrespondents, respondents were younger, but no significant differences by sex or race and ethnicity were found.
Clinicians, policy makers, and developers should be aware of patients’ views regarding AI. Patients may benefit from education on how AI is being incorporated into care and the extent to which clinicians rely on AI to assist with decision-making. Future work should examine how views evolve as patients become more familiar with AI.
eAppendix 1. Supplemental Survey Methods and Pilot Testing
eAppendix 2. Patients’ Views of AI in Health Care Survey
References
- 1.Wang F, Casalino LP, Khullar D. Deep learning in medicine-promise, progress, and challenges. JAMA Intern Med. 2019;179(3):293-294. doi: 10.1001/jamainternmed.2018.7117 [DOI] [PubMed] [Google Scholar]
- 2.Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. Public vs physician views of liability for artificial intelligence in health care. J Am Med Inform Assoc. 2021;28(7):1574-1577. doi: 10.1093/jamia/ocab055 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.SSRS . Opinion panel. Accessed August 10, 2021. https://ssrs.com/opinion-panel/
- 4.American Association for Public Opinion Research . Standard definitions: final disposition case codes and outcome rates for surveys. Accessed March 30, 2022. https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eAppendix 1. Supplemental Survey Methods and Pilot Testing
eAppendix 2. Patients’ Views of AI in Health Care Survey