Attitudes towards Artificial Intelligence within Moroccan Dermatologists: A National Survey
ABSTRACT
Background:
Artificial Intelligence (AI) is a burning topic and use of AI in our day-to-day life has increased exponentially. AI is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. Little is known about the attitudes towards AI among Moroccan dermatologists.
Objective:
The purpose of this cross-sectional study was to evaluate the attitudes of Dermatologists in our country towards AI.
Methods:
An online survey was distributed through Google Forms to Moroccan Dermatologists and was open from January to June 2021. Statistical analysis of the data collected was performed using Jamovi software. Thus any association for which the p-value was less than 0.05 is considered statistically significant.
Results:
In total, 183 surveys were completed and further analyzed. Overall 79,8% of respondents were female, and the median age was 35 years (interquartile range 25-74). 30.6% stated that they were not aware about AI and about 34,4 % had basic knowledge about AI technologies. Only 7.7% of the respondents strongly agreed that the human dermatologist will be replaced by AI in the foreseeable future. For the entire group, 61.8% of the respondents agreed or strongly agreed that AI will improved dermatology, and 70% thought that AI should be a part of medical training. In addition only 32.2% have reported having read publications about AI. Female dermatologists showed more fear about the use of AI within dermatology (p=0.011) and suggested that AI will have a very strong potential in detection of skin diseases based on dermoscopic images (p=0.025).
Conclusions:
Our results demonstrate an overall optimistic attitude towards AI among Moroccan dermatologists. The majority of respondents believe it will improve our diagnostic capabilities.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.