23 Jun Skin Cancer Recognition Enhanced By Human-Computer Collaboration
MedicalResearch.com Interview with:
Professor Harald Kittler, MD
ViDIR Group, Department of Dermatology
Medical University of Vienna
MedicalResearch.com: What is the background for this study? What types of skin cancers were assessed? (melanoma, SCC, Merkel etc).
Response: Some researchers believe that AI will make human intelligence dispensable. It is, however, still a matter of debate how exactly AI will influence diagnostic medicine in the future.
The current narrative is focused on a competition between human and artificial intelligence. We sought to shift the direction of this narrative more towards human/AI collaboration. To this end we studied the use-case of skin cancer diagnosis including the most common types of skin cancer such as melanoma, basal cell- and squamous cell carcinoma. The initial idea was to explore the effects of varied representations of AI support across different levels of clinical expertise and to address the question of how humans and machines work together as a team.
MedicalResearch.com: What are the main findings?
Response: We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We show that AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts.
Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis.
MedicalResearch.com: What should readers take away from your report?
Response: In the field of skin cancer diagnosis AI-based triage and decision support could assist readers in managing workloads and expanding their performance. In contrast to the current narrative, our findings suggest that the primary focus should shift from human–computer competition to human–computer collaboration.
MedicalResearch.com: What recommendations do you have for future research as a result of this work?
Response: The performance of AI-based systems should be tested under real-world conditions in the hands of the intended users and not as stand-alone devices
Tschandl, P., Rinner, C., Apalla, Z. et al. Human–computer collaboration for skin cancer recognition. Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0
The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition. Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.