Applying ethics to AI in the workplace: the design of a scorecard for Australian workplace health and safety.

Artificial Intelligence (AI) is taking centre stage in economic growth and business operations alike. Public discourse about the practical and ethical implications of AI has mainly focussed on the societal level. There is an emerging knowledge base on AI risks to human rights around data security and privacy concerns. A separate strand of work has highlighted the stresses of working in the gig economy. This prevailing focus on human rights and gig impacts has been at the expense of a closer look at how AI may be reshaping traditional workplace relations and, more specifically, workplace health and safety. To address this gap, we outline a conceptual model for developing an AI Work Health and Safety (WHS) Scorecard as a tool to assess and manage the potential risks and hazards to workers resulting from AI use in a workplace. A qualitative, practice-led research study of AI adopters was used to generate and test a novel list of potential AI risks to worker health and safety. Risks were identified after cross-referencing Australian AI Ethics Principles and Principles of Good Work Design with AI ideation, design and implementation stages captured by the AI Canvas, a framework otherwise used for assessing the commercial potential of AI to a business. The unique contribution of this research is the development of a novel matrix itemising currently known or anticipated risks to the WHS and ethical aspects at each AI adoption stage.

Assessment of a smartphone-based application for diabetic foot ulcer measurement.

The accurate measurement of diabetic foot ulcer (DFU) wound size is essential as the rate of wound healing is a significant prognostic indicator of the likelihood of complete wound healing. Mobile phone photography is often used for surveillance and to aid in telemedicine consultations. However, there remains no accurate and objective measurement of wound size integrated into these photos. The NDKare mobile phone application has been developed to address this need and our study evaluates its accuracy and practicality for DFU wound size assessment. The NDKare mobile phone application was evaluated for its accuracy in two- (2D) and three-dimensional (3D) wound measurement. One hundred and fifteen diabetic foot wounds were assessed for wound surface area, depth and volume accuracy in comparison to Visitrak and the WoundVue camera. Thirty five wounds had two assessors with different mobiles phones utilizing both applications to assess the reproducibility of the measurements. The 2D surface area measurements by NDKare showed excellent concordance with Visitrak and WoundVue measurements (ICC: 0.991 [95% CI: 0.988, 0.993]) and between different users (ICC: 0.98 [95% CI: 0.96, 0.99)]. The 3D NDKare measurements had good agreement for depth and fair agreement for volume with the WoundVue camera. The NDKare phone application can consistently and accurately obtain 2D measurements of diabetic foot wounds with mobile phone photography. This is a quick and readily accessible tool which can be integrated into comprehensive diabetic wound care.

Evaluation of a Novel Three-Dimensional Wound Measurement Device for Assessment of Diabetic Foot Ulcers.

Objective: The initial wound measurement and regular monitoring of diabetic foot ulcers (DFU) is critical to assess treatment response. There is no standardized, universally accepted, quick, reliable, and quantitative assessment method to characterize DFU. To address this need, a novel topographic imaging system has been developed. Our study aims at assessing the reliability and practicality of the WoundVue® camera technology in the assessment of DFU. Approach: The WoundVue system is a prototype device. It consists of two infrared cameras and an infrared projector, and it is able to produce a three-dimensional (3D) reconstruction of the wound structure. Fifty-seven diabetic foot wounds from patients seen in a multidisciplinary foot clinic were photographed from two different angles and distances by using the WoundVue camera. Wound area, volume, and maximum depth were measured for assessment of reliability. Thirty-one of these wounds also had area calculated by using the established Visitrak™ system, and a correlation between the area obtained by using both systems was assessed. Results: WoundVue images analysis showed excellent agreement for area (intraclass correlation coefficient [ICC]: 0.995), volume (ICC: 0.988), and maximum depth (ICC: 0.984). Good agreement was found for area measurement by using the WoundVue camera and Visitrak system (ICC: 0.842). The average percentage differences between measures obtained by using the WoundVue from different angles for assessment of different sizes and shapes of wounds were 2.9% (95% confidence interval [CI]: 0.3-5.4), 12.9% (95% CI: 9.6-35.7), and 6.2% (95% CI: 2.3-14.7) for area, maximum depth, and volume, respectively. Innovation: This is the first human trial evaluating this novel 3D wound measurement device. Conclusion: The WoundVue system is capable of recreating a 3D model of DFU and produces consistent data. Digital images are ideal for monitoring wounds over time, and the WoundVue camera has the potential to be a valuable adjunct in diabetic foot wound care.

Determining ellipses from low-resolution images with a comprehensive image formation model.

When determining the parameters of a parametric planar shape based on a single low-resolution image, common estimation paradigms lead to inaccurate parameter estimates. The reason behind poor estimation results is that standard estimation frameworks fail to model the image formation process at a sufficiently detailed level of analysis. We propose a new method for estimating the parameters of a planar elliptic shape based on a single photon-limited, low-resolution image. Our technique incorporates the effects of several elements-point-spread function, discretization step, quantization step, and photon noise-into a single cohesive and manageable statistical model. While we concentrate on the particular task of estimating the parameters of elliptic shapes, our ideas and methods have a much broader scope and can be used to address the problem of estimating the parameters of an arbitrary parametrically representable planar shape. Comprehensive experimental results on simulated and real imagery demonstrate that our approach yields parameter estimates with unprecedented accuracy. Furthermore, our method supplies a parameter covariance matrix as a measure of uncertainty for the estimated parameters, as well as a planar confidence region as a means for visualizing parameter uncertainty. The mathematical model developed in this paper may prove useful in a variety of disciplines that operate with imagery at the limits of resolution.