ChatGPT and dermatology: friends or foes?

Advancing dermatology: embracing the power of artificial intelligence. Comment on 'Artificial intelligence-assisted publishing in five prominent dermatology journals: a quantitative study of recent publications'

This is a comment on an article published in CED entitled ‘Artificial intelligence-assisted publishing in five prominent dermatology journals: a quantitative study of recent publications’ by Heerfordt. The author conducted a study investigating current patterns in artificial intelligence (AI)-generated content specifically within abstracts of articles in five prominent dermatology journals. The primary outcome was the likelihood of abstracts being AI-generated but none of 57 abstracts selected were found to be possibly or likely AI-generated. Here we want to report our opinion about using AI in the field of dermatology.

The Past, the Present and the Future of Teledermatology: A Narrative Review

Teledermatology may be defined as the application of telemedicine to dermatology. According to published data, teledermatology is more widespread in Europe and North America, probably where resources for health care are greater than in other areas of the world. Indeed, teledermatology requires advanced technology to be efficient, as high image quality is necessary to allow the dermatologist to make correct diagnoses. Thanks to the recent advances in this field, teledermatology is become routinary in daily clinical practice. However, its use has been improved over time, overcoming several challenges. The aim of this narrative review is to retrace the almost 30-year history of teledermatology, to address the new challenges posed by advancing technologies such as artificial intelligence and the implications it may have on healthcare.

Utilizing Instagram as a novel method for dermatopathology instruction

Instagram provides an accessible and interactive platform for the dissemination of medical knowledge. The Instagram platform is ideally curated for intrinsically visual specialties such as dermatopathology. The described methodology highlights Instagram’s expansive scope and potential to promote active learning in dermatopathology education. Eastern Virginia Medical School’s Instagram page @evmsdermatology provides a novel case framework for further reproducibility throughout similar medical specialties.

Public Perceptions, Factors, and Incentives Influencing Patient Willingness to Share Clinical Images for Artificial Intelligence-Based Healthcare Tools

INTRODUCTION

The use of artificial intelligence (AI) as a diagnostic and decision-support tool is increasing in dermatology. The accuracy of image-based AI tools is incumbent on images in training sets, which requires patient consent for sharing. This study aims to understand individuals' willingness to share their images for AI and variables that influence willingness.

METHODS

In an online survey administered via Amazon Mechanical Turk, sketches of the hand, face, and genitalia assigned to two use cases employing AI (research vs. personal medical care) were shown. Participants rated willingness to share the image on a 7-point Likert scale.

RESULTS

Of the 1010 participants, individuals were most willing to share images of their hands (81.2%), face (70.3%), and lastly genitals (male: 56.8%, female: 46.7%). Individuals were more willing to share for personal care versus research (OR 0.77 [95% CI 0.69-0.86]). Willingness to share was higher among males, participants with higher education, tech-savvy participants, and frequent social media users. Most participants were willing to share images if offered monetary compensation, with face images requiring the highest payment (mean $18.25, SD 20.05). Only 38.7% of individuals refused image sharing regardless of any monetary compensation, with the majority of this group unwilling to share images of the genitals.

CONCLUSIONS

This study demonstrates overall public support for sharing images to AI-based tools in dermatology, with influencing factors including image type, context, education level, technology comfort, social media use, and monetary compensation.