Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world's most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations' 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good. T he challenges facing our world today have grown in complexity and increasingly require large, coordinated efforts: between countries; and across a broad spectrum of governmental and non-governmental organisations (NGOs) and the communities they serve. These coordinated efforts work towards supporting the Sustainable Development Goals (SDGs) 1 , and there continues to be an important role for technology to support the developmental organisations and efforts active in this field to deliver the highest impact. Artificial intelligence (AI) and machine learning (ML) have attracted widespread interest in recent years due to a series of high-profile successes. AI has shown success in games and

Purpose of Review Out-of-hospital cardiac arrest (OHCA) remains a significant health problem in the USA and only 8.6% of victims survive with good neurological function, despite advances in emergency cardiac care. The likelihood of OHCA survival decreases by 10% for every minute without resuscitation. Recent Findings Automatic external defibrillators (AEDs) have the potential to save lives yet public access defibrillators are underutilized (< 2% of the time) because they are difficult to locate and rarely available in homes or residential areas, where the majority (70%) of OHCA occur. Even when AEDs are within close proximity (within 100 m), they are not used 40% of the time.

BACKGROUND Despite evidence linking rapid defibrillation to out-of-hospital cardiac arrest (OHCA) survival, bystander use of automatic external defibrillators (AEDs) remains low, due in part to AED placement and accessibility. AED-equipped drones may improve time-to-defibrillation, yet the benefits and costs are unknown. METHODS We designed drone deployment networks for the state of North Carolina using mathematical optimization models to select drone stations from existing infrastructure by specifying the number of stations and the targeted AED arrival time. Expected outcomes were evaluated over the drone’s lifespan (4 years). We estimated the following parameters: proportion of OHCAs within a targeted AED delivery time, bystander utilization of AEDs, survival/neurological status, and incremental cost per quality-adjusted life year (QALY). RESULTS Statewide, 16,503 adults aged 18 or older were expected to experience OHCA with an attempted resuscitation over 4 years. Compared to no drone network, all proposed drone networks were expected to improve survival outcomes. For example, assuming 46% of OHCAs have bystanders willing to use an AED, a 500-drone network decreased the median time of defibrillator arrival from 7.7 to 2.7 minutes compared to no drone network. Expected survival rates doubled (24.5% versus 12.3%), resulting in an additional 30,267 QALYs ($858/incremental QALY). If just 4.5% of OHCAs had willing bystanders, 13.8% of victims would have survived. Sensitivity analysis demonstrated that an AED drone network remained cost-effective over a wide range of assumptions. CONCLUSI0NS With proper integration into existing systems, large-scale networks for drone AED delivery have the potential to substantially improve OHCA survival rates while remaining cost-effective. Public health researchers should consider advocating for feasibility studies and policy development surrounding drones.

Detect and Avoid (DAA) systems are complex communication and locational technologies comprising multiple independent components. DAA technologies support communications between ground-based and space-based operations with aircraft. Both manned and unmanned aircraft systems (UAS) rely on DAA communication and location technologies for safe flight operations. We examined the occurrence and duration of communication losses between radar and automatic dependent surveillance-broadcast (ADS-B) systems with aircraft operating in proximate airspace using data collected during actual flight operations. Our objectives were to identify the number and duration of communication losses for both radar and ADS-B systems that occurred within a discrete time period. We also investigated whether other unique communication behavior and anomalies were occurring, such as reported elevation deviations. We found that loss of communication with both radar and ADS-B systems does occur, with variation in the length of communication losses. We also discovered that other unexpected behaviors were occurring with communications. Although our data were gathered from manned aircraft, there are also implications for UAS that are operating within active airspaces. We are unaware of any previously published work on occurrence and duration of communication losses between radar and ADS-B systems.

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Functional status is a major contributor to overall health and reflects both daily activity level (performance) and maximum attainable activity level (capacity). Existing assessment tools evaluate only 1 domain of function and do not provide insight into contributors to functional decline. We addressed these deficiencies by developing the Tennessee Functional Status Questionnaire (TFSQ), which reports activity levels in metabolic equivalents (METs) and evaluates 5 key areas: performance, capacity, activity, pain, and acute care. We validated the activity levels reported by the TFSQ against the Duke Activity Status Index (DASI).Methods: In this prospective, observational study, 120 patients completed both the TFSQ and the DASI. TFSQ-reported functional performance and capacity was correlated with DASI-calculated METs.Results: Pearson correlation between TFSQ-reported capacity and DASI-calculated METs was r = 0.69, P < .001. TFSQ capacity was significantly lower in patients who reported recently decreased activity, pain affecting function, or recent acute care exposure.Conclusions: The TFSQ is a brief and efficient assessment of patient function, standardized to METs and validated against the DASI. Our study suggests that many patients may have the functional reserve to increase daily physical activity and that factors such as changes in activity, pain, and recent acute care interaction may lower functional capacity.

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