In decision support applications of AI, the AI algorithm's output is framed as a suggestion to a human user. The user may ignore this advice or take it into consideration to modify their decision. With the increasing prevalence of such human-AI interactions, it is important to understand how users react to AI advice. In this paper, we recruited over 1100 crowdworkers to characterize how humans use AI suggestions relative to equivalent suggestions from a group of peer humans across several experimental settings. We find that participants' beliefs about how human versus AI performance on a given task affects whether they heed the advice. When participants do heed the advice, they use it similarly for human and AI suggestions. Based on these results, we propose a two-stage, "activation-integration" model for human behavior and use it to characterize the factors that affect human-AI interactions.

For the deployment of artificial intelligence (AI) in high risk settings, such as healthcare, methods that provide interpretability/explainability or allow fine-grained error analysis are critical. Many recent methods for interpretability/explainability and fine-grained error analysis use concepts, which are meta-labels which are semantically meaningful to humans. However, there are only a few datasets that include concept-level meta-labels and most of these meta-labels are relevant for natural images that do not require domain expertise. Previous densely annotated datasets in medicine focused on meta-labels that are relevant to a single disease such as osteoarthritis or melanoma. In dermatology, skin disease is described using an established clinical lexicon that allow clinicians to describe physical exam findings to one another. To provide the first medical dataset densely annotated by domain experts to provide annotations useful across multiple disease processes, we developed SkinCon: a skin disease dataset densely annotated by dermatologists. SkinCon includes 3230 images from the Fitzpatrick 17k skin disease dataset densely annotated with 48 clinical concepts, 22 of which have at least 50 images representing the concept. The concepts used were chosen by two dermatologists considering the clinical descriptor terms used to describe skin lesions. Examples include "plaque", "scale", and "erosion". These same concepts were also used to label 656 skin disease images from the Diverse Dermatology Images dataset, providing an additional external dataset with diverse skin tone representations. We review the potential applications for the SkinCon dataset, such as probing models, concept-based explanations, concept bottlenecks, error analysis, and slice discovery. Furthermore, we use SkinCon to demonstrate two of these use cases: debugging mistakes of an existing dermatology AI model with concepts and developing interpretable models with post-hoc concept bottleneck models.

While artificial intelligence (AI) holds promise for supporting healthcare providers and improving the accuracy of medical diagnoses, a lack of transparency in the composition of datasets exposes AI models to the possibility of unintentional and avoidable mistakes. In particular, public and private image datasets of dermatological conditions rarely include information on skin color. As a start towards increasing transparency, AI researchers have appropriated the use of the Fitzpatrick skin type (FST) from a measure of patient photosensitivity to a measure for estimating skin tone in algorithmic audits of computer vision applications including facial recognition and dermatology diagnosis. In order to understand the variability of estimated FST annotations on images, we compare several FST annotation methods on a diverse set of 460 images of skin conditions from both textbooks and online dermatology atlases. These methods include expert annotation by board-certified dermatologists, algorithmic annotation via the Individual Typology Angle algorithm, which is then converted to estimated FST (ITA-FST), and two crowd-sourced, dynamic consensus protocols for annotating estimated FSTs. We find the inter-rater reliability between three board-certified dermatologists is comparable to the inter-rater reliability between the board-certified dermatologists and either of the crowdsourcing methods. In contrast, we find that the ITA-FST method produces annotations that are significantly less correlated with the experts' annotations than the experts' annotations are correlated with each other. These results demonstrate that algorithms based on ITA-FST are not reliable for annotating large-scale image datasets, but human-centered, crowd-based protocols can reliably add skin type transparency to dermatology datasets. Furthermore, we introduce the concept of dynamic consensus protocols with tunable parameters including expert review that increase the visibility of crowdwork and provide guidance for future crowdsourced annotations of large image datasets.

There is great variation in drug-response phenotypes, and a "one size fits all" paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

MOTIVATION: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics. RESULTS: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. CONTACT: [email protected]