Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey

Abstract

Background: Artificial intelligence (AI) has recently surfaced as a research topic in dermatology and dermatopathology. In a recent survey, dermatologists were overall positive toward a development with an increased use of AI, but little is known about the corresponding attitudes among pathologists working with dermatopathology. The objective of this investigation was to make an inventory of these attitudes.Participants and Methods: An anonymous and voluntary online survey was prepared and distributed to pathologists who regularly analyzed dermatopathology slides/images. The survey consisted of 39 question divided in five sections; (1) AI as a topic in pathology; (2) previous exposure to AI as a topic in general; (3) applications for AI in dermatopathology; (4) feelings and attitudes toward AI and (5) self-reported tech-savviness and demographics. The survey opened on March 13, 2020 and closed on May 5, 2020.Results: Overall, 718 responders (64.1% females) representing 91 countries were analyzed. While 81.5% of responders were aware of AI as an emerging topic in pathology, only 18.8% had either good or excellent knowledge about AI. In terms of diagnosis classification, 42.6% saw strong or very strong potential for automated suggestion of skin tumor diagnoses. The corresponding figure for inflammatory skin diseases was 23.0% (P adj <0.0001). For specific applications, the highest potential was considered for automated detection of mitosis (79.2%), automated suggestion of tumor margins (62.1%) and immunostaining evaluation (62.7%). The potential for automated suggestion of immunostaining (37.6%) and genetic panels (48.3%) were lower. Age did not impact the overall attitudes toward AI. Only 6.0% of the responders agreed or strongly agreed that the human pathologist will be replaced by AI in the foreseeable future. For the entire group, 72.3% agreed or strongly agreed that AI will improve dermatopathology and 84.1% thought that AI should be a part of medical training.Polesie et al. Attitudes Toward AI Within DematopathologyConclusions: Pathologists are generally optimistic about the impact and potential benefit of AI in dermatopathology. The highest potential is expected for narrow specified tasks rather than a global automated suggestion of diagnoses. There is a strong need for education about AI and its use within dermatopathology.

Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

“…Artificial intelligence (AI) was first introduced some years ago, but in recent years, there has been increasing exploration of the utility and cost-saving of such technology (1,2). AI brings about great potential in changing existing healthcare practice, from prevention, screening, diagnosis, treatment, and care (2,3).…”

Artificial Intelligence in Dermatopathology: New Insights and Perspectives

“…Increasing attention is being paid to the applications of AI and ML in the diagnosis of simple or more complex skin lesions, and the training of AI algorithms is gathering increasing feedback from the scientific community [ 4 , 5 , 6 , 7 ]. In a recent paper by Sam Polesie et al [ 8 ], an attempt was made to understand the degree of perception, attitude and knowledge of AI in general and then as applied to dermatology and dermatopathology. An anonymous, voluntary online survey was prepared and distributed to pathologists who regularly analyze dermatopathology slides/images.…”

As Novas Faces Da Saúde: Tecnologia E Multidisciplinaridade No Contexto Atual

“…Através de sistemas programados é possível por meio de algoritmos classificar imagens segundo uma ampla variedade de doenças de forma rápida e eficaz. Estudos recentes demonstraram um alto potencial na identificação de margens tumorais para delimitação entre áreas comprometidas ou não com lesões malignas (POLESIE et al, 2020). Entretanto, vale ressaltar que tais aplicativos só podem ser utilizados de forma precisa em populações para as quais estes foram previamente treinados, consequentemente, esse cenário tem limitado a utilização da IA para diagnósticos em larga escala visto que se treinados em um grupo populacional e aplicados em outro, os resultados são sujeitos a induções sistemáticas levando a um diagnóstico incorreto (GOMOLIN et al, 2020).…”