METHODS
An electronic survey was created.
RESULTS
A total of 108 radiation oncologists participated. One-fourth (24.3%) rated their knowledge of AI as
very poor. The majority (94%) reported that they need training about AI. Most respondents (62.6%)
indicated that they had never used any AI application. Nearly 90% reported that the introduction of
AI would improve RO. Image analysis and target definition were identified as key benefits of AI in RO
by 84% of respondents. The medical liability due to machine error and black box uncertainties was the
greatest concerns. The need for clinical validation of AI applications, development of ethical frameworks,
and medicolegal guidelines was identified as priorities before the implementation of AI in RO by
86%, 78%, and 68%, respectively.
CONCLUSION
There was a big gap in knowledge within our RO community. The enthusiasm to learn was high. AI
applications have not been imposed much in clinical routine. Mostly, the participants felt optimistic
about the introduction of AI. The top areas where AI was thought to be most useful in RO were reliant
on imaging. The respondents were mostly concerned about the medical liability.
Keywords: Artificial intelligence; radiation oncology; radiotherapy; survey
Besides, areas of medicine that are most reliant on
imaging will be amongst the first to be impacted by
AI.[
In this study, we conducted a national survey to
ascertain radiation oncologists" perception of AI technologies,
their current use of AI-based models in their
clinical practice, and their expectations, concerns, and
wishes in terms of the future of RO in the era of AI.
The study received approval from Dr. Suat Seren Chest Diseases and Research Hospital Ethics Board in June 2021. An electronic survey was created. The E-mail with the link of the survey to participate was distributed among TROD members through TROD in July 2021. The electronic informed consent was obtained from each participant online before survey commencement. The responses were collected in September 2021 and then analyzed using descriptive statistics. A copy of questionnaire has not been included in this paper, but the full version is available on request.
The Current Knowledge and use of AI in Clinical
Practice
One-third of participants (33%) rated their knowledge
of AI as below the average, and 24.3% as very poor,
only 3% reported as excellent (Fig.
Perceived Impact of AI on RO
Nearly 90% of respondents reported that the introduction
of AI would improve the field of RO (Fig.
The top five areas where AI is thought to be most
useful in RO were (1) image analysis, (2) target definition,
(3) on-line adaptive therapy, (4) treatment planning,
and (5) synthetic reconstruction (Fig.
Perceived Advantages and Concerns of AI
The top three expected potential advantages of AI were
as follows: (1) more personalized and evidence-based
treatment approach, (2) improved imaging quality, and
(3) improved therapeutic gain (Fig.
AI: Artificial intelligence.
The top three ranked potential concerns about AI
were (1) medical liability due to machine error, (2) black box uncertainties, and (3) ethical violation and
negative impact on workforce needs (Fig.
The radiation oncologists feel fear (2%), threatening (5%), anxiety (18%), confidence (27%), and hope (85%) about the future of RO in the new era of AI.
Acceptable AI Performance Standards and Clinical
Workflows
To be able to have a place in clinical practice, 37.5%
of respondents stated that AI applications would need
to achieve performance that was superior to average
performing radiation oncologist, 34.4% stated as
equivalent to the best, and 22% stated as superior to
the best (Fig.
Preparedness for the Future
Most of radiation oncologists (94%) reported that they
need training and education about AI. The most preferred
methods of education were as follows: regular
sessions on understanding of practical implications of
AI (78%), guidelines for the clinicians about latest developments
(78%), fundamentals of AI (62.5%), and safety
of these technologies in the context of global technology
giants (53%). Furthermore, there was a demand for collaborative
courses with technological companies (59%).
The reported needs before AI implementation in routine clinical practice were clinical validation of AI applications before being introduced into clinical practice (86%), development of ethical frameworks for AI implementation (78%), definition of responsibilities of clinicians (70%), and development of medicolegal guideline to set the responsibility if error has been made based on information provided by AI (68%).
Seventy 5% of respondents felt that AI applications would help and support doctors, 67% believed that it would increase the doctors" performance, 57% implicated that those clinicians who use AI in their daily practice would have a positive impact compared to none users, 50% expected that the doctors would guide AI, 11% felt that AI would replace clinicians, and 5% believed that the income and welfare level of doctors would be better.
While 44% of respondents believed that AI will be directed by technological companies in the future, 43% believed that it would be directed in cooperation of clinicians and companies.
Instead of perceiving AI as a threat, the rate of radiation oncologists who think that they can actively shape and be part of this active transformation is 76%.
There was very low representation from RO attendees. The reason behind these results is not clear, but this finding might imply a need for rapid action to advance learning from earlier stages in their education for radiation trainees, representing the future generation of practitioners.
Considering the best method to learn AI algorithms,
responses were in line with other surveys; the
participants highlighted, mainly a need for regular
training and education sessions about fundamentals of
AI and latest developments.[
In the current state, the use of AI-based methods
in daily practice is very low in our community. Low
rates of clinical AI use by other disciplines have previously been reported, indicating that AI has not yet
been widely adapted in clinics.[
Mostly, the participants felt optimistic about the
introduction of AI applications into the field of RO.
Most of the other surveys had similar positive sentiments;
respondents indicated that most clinicians believed
that AI technology would have a positive impact
in their profession.[
The top areas where AI was thought to be most
useful in RO were reliant on imaging. This finding is
not surprising since the image analysis is a core work
task for RO.[
Although three quarters of radiation oncologists
predicted that the workforce needs will be decreased,
the reduced reliance on clinicians was not the primary
concern; it has the least first-degree importance when
considering the potential disadvantages of AI. Accordingly,
the rate of participants who considered that AI
would replace clinicians was only 11% which was quite
a different finding than the Canadian study that 34% of
participants were afraid of losing their jobs.[
The respondents were mostly concerned about the
medical liability due to machine errors and black box
uncertainties which were a similar finding with the
literature.[
The adaption of AI technologies by radiation oncologists
seems to be influenced by the level of performance
of AI tools, because our respondents consider
that AI systems should have error levels at least superior
to the average performing radiation oncologist.
Similarly, Scheetz et al. reported that their respondents
had high expectations of AI performance also. Nevertheless,
they seem mostly ready to adapt and cooperate
with AI implementations in their clinical workflows.
Like any new technology, the need for clinical
validation of AI applications, development of ethical
frameworks, and medicolegal guidelines was consistent priorities for clinicians before AI implementation
into clinical practice. An urgent need for clarity regarding
these concerns is obvious since legal, moral, and
ethical considerations are increasingly challenging as
technologies become more autonomous. If these concerns
would be clarified in the near future this might
help to decrease the anxiety among clinicians against
AI systems. The methods of integration of AI systems
into RO practice should be evaluated also.
Although the future of AI technology in RO is
challenging to predict, we believe that we might have
helped to improve the awareness of radiation oncologists
about the integration of AI as a cooperative tool
in RO profession in our country.
Limitations of the Study
The limitations of this survey warrant consideration.
As a result of volunteer response bias, the results may
not be broadly representative of the views of all radiation
oncologists in our country and may not be generalizable
to other countries. Moreover, there was low
representation from RO attendees. The reason behind
these results is not clear, but this finding might imply a
need for rapid action to advance learning from earlier
stages in their education for radiation trainees, representing
the future generation of practitioners. Finally,
the limitations on the scope of response options, due
to design of survey, limit us about the comprehensive
understanding of the perceptions of respondents.
Peer-review: Externally peer-reviewed.
Conflict of Interest: All authors declared no conflict of interest.
Ethics Committee Approval: The study was approved by the Dr. Suat Seren Chest Diseases and Surgery Training and Research Hospital Ethics Committee (no: E-49109414-604- 02, date: 11/08/2021).
Financial Support: None declared.
Authorship contributions: Concept - E.K.K., Ş.B.G.; Design - E.K.K.; Supervision ? D.E., E.Ö.; Funding - None; Materials - E.K.K.; Data collection and/or processing - E.K.K., E.Y.E.; Data analysis and/or interpretation - E.K.K., Ş.B.G.; Literature search - E.K.K., Ş.B.G.; Writing - E.K.K.; Critical review - Ş.B.G., D.E., E.Ö.
