October 2021 - Christoph Rinner

Priv.-Doz. DI Dr. Christoph Rinner

MedUni Wien RESEARCHER OF THE MONTH October 2021

In 2018 Harald Kittler, Philipp Tschandl and Christoph Rinner created the learning and teaching platform DermaChallenge. Using the concept of gamification the platform now has over 5000 registered users from over 60 countries. The researchers used data collected in DermaChallenge to analyse the diagnostic accuracy of collectives of humans and how AI influences and best supports clinicians in future tele dermatology settings. It was showed for example that groups of non-experts could achieve expert level accuracy. The reputation of the DermaChallenge platform allowed showing in a Nature Medicine article that high-quality AI-based clinical decision support improves clinicians' diagnostic accuracy and that the least experienced clinicians benefit the most from AI-based support. Expert raters benefited only slightly from AI support and only when they were not confident with their initial diagnosis.

Selected Literature

1. Sinz C, Tschandl P, Rosendahl C, Akay BN, Argenziano G, Blum A, et al. Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. J Am Acad Dermatol. 2017;77(6):1100-9.
2. Tschandl P, Rosendahl C, Akay BN, Argenziano G, Blum A, Braun RP, et al. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatol. 2019;155(1):58-65.
3. Tschandl P, Codella N, Akay BN, Argenziano G, Braun RP, Cabo H, et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019;20(7):938-47.
4. Rinner C, Kittler H, Rosendahl C, Tschandl P. Analysis of Collective Human Intelligence for Diagnosis of Pigmented Skin Lesions Harnessed by Gamification Via a Web-Based Training Platform: Simulation Reader Study. J Med Internet Res. 2020;22(1):e15597.
5. Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A, et al. Human-computer collaboration for skin cancer recognition. Nat Med. 2020;26(8):1229-34.