Respondent demographics

Survey responses were collected from a total of 487 respondents in 59 countries (Fig. 1a). The majority of respondents practiced in Canada (119, 24.9%), USA (106, 22.2%), and the UK (50, 10.5%). Respondent age was spread over a wide range of age groups, with most frequent age group being 30–35 (97, 20.0%), and relatively smaller numbers below the age of 30 (45, 9.30%) and over the age of 66 (25, 5.2%) (Fig. 1b). We received a slightly increased number of male respondents (260, 53.7%) compared to female respondents (223, 46.1%). 23.7% of respondents reported having practiced less than 5 years, 19.3% between 6 and 10 years, and relatively fewer respondents had practiced for longer periods of time (Fig. 1d). Most respondents were practicing pathologists (241, 49.6%) or residents/fellows (124, 25.5%). While a minority of respondents answered “Other” (31, 6.4%), some of the responses included positions that could be grouped with “practicing pathologist”. Most respondent specialties were reported as general pathology, anatomic pathology, neuropathology, or multiple sites/other. Most were academic pathologists (342, 70.5%), relatively fewer were community pathologists (103, 21.2%), and the remaining practiced within other settings including government, military, or industry. Respondents were overall more likely to come from larger practices, with most coming from groups with >25 members (148, 30.5%), and relatively fewer from smaller practices with only 29 (6.0%) from groups of 21–25, 52 (10.7%) from groups of 16–20, 83 (17.1%) from groups of 11–15, 100 (20.6%) from groups of 5–10, and 67 (13.8%) from groups of less than 5. Respondent results are summarized below, and select graphical depictions can be found in Fig. 2.

Fig. 1 Demographic data of survey respondents. a City of pathology practice/training of all survey respondents (473 responses, 14 skipped), (Maps data ©2018 Google). b Age distribution of survey respondents (484 responses, 3 skipped). c Gender demographic of respondents (484 responses, 3 skipped). d Years of pathology practice (including training) of survey respondents (481 responses, 6 skipped). e Reported positions held by survey respondents. Some of the responses within “Other” included positions that could be grouped within “Practicing Pathologist”, but are presented separately as described by respondents (486 responses, 1 skipped) Full size image

Fig. 2 Opinions on the use of AI in clinical pathology practice. a Predicted interval to implementation in personal practice (480 responses, 7 skipped). b Predicted interval to implementation in routine practice (480 responses, 7 skipped). c Level of concern that pathologists will be displaced by AI tools (484 responses, 3 skipped). d Pathologists’ perspectives on impact of AI tools on personal efficiency (483 responses, 4 skipped). e Pathologists’ perspectives on cost-effectiveness of AI tools implementation (481 responses, 6 skipped) Full size image

General attitudes and perspectives

Overall, respondent attitudes towards AI in diagnostic pathology were positive, with many either expressing interest in (119, 41.2%), or excitement about (155, 32.1%) the integration of AI-tools. Overall, concerns about displacement and negative career impacts were limited; many felt AI would not impact employability (184, 38.0%) or would create new positions and increase employment prospects (205, 42.4%). A smaller number reported being concerned (85, 17.6%) or extremely concerned (10, 2.1%) that AI-tools would displace human jobs. Most respondents did not feel AI-tools would impact compensation (317, 65.6%), though a minority felt compensation would be negatively impacted (80, 16.6%). Overall, many respondents predicted integration of AI into diagnostic workflow within the next five years (191, 39.8%) or ten years (196, 40.8%), with the remaining predicting implementation within 15 years (51, 10.6%) or greater than 20 years (23, 4.8%). Regarding impacts of AI-tool implementation on relationships with colleagues, most respondents either felt that use of AI-tools would not impact the way they were viewed by colleagues (210, 43.6%), or that their adoption of new technology would be welcomed (197, 40.9%). A minority felt that implementation would have a negative impact on the way they were viewed by colleagues (75, 15.6%). Regarding feelings about patient perspectives, most believed patients would have no opinion (246, 51.0%) or would be excited about its use (140, 29.1%).

Perspectives on clinical implications of AI-tool use

Many respondents felt that with appropriate training, AI tools could increase (280, 58.0%) or even dramatically increase (66, 13.7%) diagnostic efficiency. A relatively smaller cohort felt efficiency would not be impacted (30, 6.2%), would be negatively impacted (4, 0.8%), or were unsure of impact on efficiency (103, 21.3%). Despite these positive attitudes towards AI tools, most respondents still felt that diagnostic decision making should remain a predominantly human task (231, 48.3%), or shared equally with an AI algorithm (121, 25.3%), while a smaller percentage felt that AI-tools should take a dominant role (97, 20.3%). Similarly, regarding the possibility of machine error, many respondents acknowledged concerns about the possibility of unpredictable results or artefact-related errors. Relatively few respondents did not feel concerned about AI-tool errors (119, 24.8%), or believed AI error rates would be lower compared to humans (48, 10.0%). Overall, a majority of pathologists felt that the use of AI-tools combined with human inputs for generation of diagnostic reports would help to decrease the rates of reporting errors (256, 53.0%). When asked about the role of AI-tools in quality assurance (QA) initiatives at their institutions, many felt AI would provide an additional level of QA (330, 68.6%), while relatively fewer felt AI-tools would have no impact on QA (120, 25.0%). Finally, in terms of medico-legal responsibility for diagnostic errors made by a human/AI combination, opinions ranged considerably, with 209 (43.7%) believing the platform vendor and pathologist should be held equally liable, 240 (50.2%) believing responsibility remained primarily that of the human, and finally 29 (6.1%) agreeing that the platform vendor should primarily be liable. These relatively split opinions suggest this still needs to be authoritatively resolved before the introduction of these tools into clinical practice.

Perspectives on impacts of AI-tools on research and trainee development

A large percentage of respondents were supportive of implementation of AI into their practices if it resulted in an increase in time spent on academic or research pursuits (448, 93.3%), compared to a minority who were not (11, 2.3%), or were unsure/other response (21, 4.4%). Nearly half of respondents anticipate that the implementation of AI will permit increased research productivity and allow pathologists to answer questions that were previously not possible (259, 53.6%), while a smaller number felt there would be no impact (99, 20.5%) or a negative impact (6, 1.24%). With respect to impacts on trainee education, many respondents felt training duration would decrease (30, 6.24%), while many others felt training duration would increase (49, 10.2%), or with the addition of training in informatics during residency/fellowship (344, 71.5%). A significant number felt that they would need to dedicate more time to training residents given an expanded tool set (119, 24.7%), with a smaller number agreeing that teaching responsibilities would decrease due to improved teaching efficiency (47, 9.8%). Interestingly, with regards to impact on clinical skills or trainees and practitioners, responses were mixed, with some respondents expressing concern that AI tools would erode pathologists’ skill (126, 26.1%), while others felt AI tools would enhance development of ‘traditional skills’ (101, 21.0%), or would not affect clinical skills (164, 34.0%).

Perspectives on implementation of AI-tools

Views on the setting in which AI-tools will be used varied, with some believing AI will be used primarily in academic settings (199, 41.3%), while the remaining majority believing usage would be similar in community and academic practices (174, 36.1%). With regards to ease of uptake and implementation, many respondents felt that AI tools will be relatively intuitive with little need for training (109, 22.7%), while others felt training from a platform representative would be of help (202, 41.1%), or a dedicated course/workshop would be necessary (139, 29.0%). This is a relatively important finding among the overwhelmingly positive respondents and signals a need for more educational resources and conferences for physician education. Relatively few believed lack of knowledge regarding AI would a pose significant difficulty (25, 5.2%), or would be an absolute barrier to implementation (5, 1.0%). In terms of preparation, many respondents had exposure to some research in the area, with 123 (25.6%) having attended/read 1-2 talks/papers on the subject, 3–5 (101, 21.0%), 5–10 (73, 15.2%), or rarely greater than 25 (45, 9.4%).

Statistically significant associations

Application of Kolmogorov–Smirnov (KS) testing revealed a number of statistically significant associations between respondent demographic characteristics and perspectives on application of AI tools in pathology. Our testing demonstrated that compared to females, male respondents carry a more optimistic outlook on integration of AI into practice (D = 0.26, n = 259, p < 0.001), and are more likely to adopt before formal validation/support attained (D = 0.27, n = 221, p < 0.001). This interest amongst male respondents could be correlated with their reported expectation that AI could improve cost-effectiveness (D = 0.12, n = 221, p < 0.01), personal efficiency (D = 0.11, n = 221, p < 0.01), and quality assurance (D = 0.17, n = 164, p < 0.001). Males also reported subjectively feeling more comfortable with adopting the technology (D = 0.15, n = 219, p < 0.001); and believed patients could become excited about AI-diagnostics with educational sessions (D = 0.15, n = 220, p < 0.001). Men were additionally more inclined to shift balance of slide interpretation towards AI-tools (D = 0.11, n = 219, p < 0.02), were more optimistic errors would become less frequent with implementation of a highly skilled independent AI tool (D = 0.19, n = 166, p < 0.001), and more likely to feel using AI tools would garner respect from colleagues (D = 0.15, n = 221, p < 0.001). Compared to community pathologists, academic pathologists were more optimistic about AI-tool integration (D = 0.14, n = 100, p < 0.001), more likely to adopt before formal validation/support attained (D = 0.25, n = 98, p < 0.001), and more likely to anticipate cost-effectiveness (D = 0.14, n = 101, p < 0.05), and quality assurance (D = 0.19, n = 83, p < 0.01). We did not find a significant difference between the distribution of male and female respondents in this later analysis to account for these differences (χ2 test, p = 0.33). When respondents were grouped into age over and under 40 years of age, respondents over 40 years old were more optimistic errors would become less frequent with implementation of a highly skilled independent (D = 0.12, n = 150, p < 0.05) or assistant AI tool (D = 0.13, n = 159, p < 0.01). Respondents over 40 predicted earlier integration of AI tools into their personal practice (D = 0.16, n = 196, p < 0.001), and pathology practice more broadly (D = 0.13, n = 197, p < 0.01). This result was somewhat unexpected and we believe it is confounded by the increased number of older respondents being male (66% (male) vs 49% (female) χ2 test, P < 0.001). Finally, given strong representation from British, American, and Canadian pathologists, we performed subgroup comparisons between these three countries. The only significant finding was that of slightly increased interest in AI technologies amongst US respondents compared to Canadian (D = 0.19, n = 114, p < 0.001), and somewhat reduced concern about potential job displacement from US respondents compared to Canadian (D = 0.16, n = 114, p < 0.01). Comparison of trainee responses (resident, fellow) to practicing respondents (staff pathologists, site directors, and department heads) failed to find significant differences. Similarly, subgroup comparisons between generalists (general pathology, anatomic pathology) and subspecialty pathologists (neuropathology, cardiovascular pathology, etc.) did not find significant associations or differences.