Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.</jats:p

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.</jats:p

Life style behavioral factors and integrative successful aging among puerto ricans living in the Mainland United States

[Background]: Few studies have assessed multidimensional models for predicting successful aging that incorporate both physical and cognitive-psychosocial elements among minority populations. This study aimed to establish a comprehensive lifestyle behavioral factors (cLBF) score and an integrative successful aging (ISA) score and explore their associations among older Puerto Rican adults. [Methods]: Data were assessed from 889 adults (45–75 years) participating in the longitudinal (baseline and 2-year follow-up) Boston Puerto Rican Health Study. Higher cLBF score (range 0–10) indicates healthier behaviors (nonsmoking, lack of sedentarism, physical activity, high diet quality, and adequate sleep). The physical domain score of ISA included 8 components (functional impairment, hypertension, diabetes, cancer, cardiovascular disease, respiratory disease, arthritis, osteoporosis) and ranged 0–11. The cognitive-psychosocial domain of ISA included 5 components (cognitive impairment, depressive symptoms, social support, perceived stress, and self-rated health) and ranged 0–10. The sum of both domains comprised the ISA score, ranging 0–21. Higher scores of ISA and its domains indicate more successful aging. [Results]: At 2 years, the mean ± SD of cLBF score was 4.9 ± 1.8, and ISA was 10.1 ± 3.3. In multivariable-adjusted models, cLBF score was significantly and positively associated with 2-year change in overall ISA (β [95% CI]: 0.15 [0.07, 0.24] points), in physical domain (0.09 [0.04, 0.13] points), and in cognitive-psychosocial domain (0.08 [0.02, 0.14] points). [Conclusions]: Maintaining healthier lifestyle behaviors may contribute to successful aging through both physical and cognitive-psychosocial domains. The results support using a multidimensional definition of successful aging in Puerto Ricans and evaluating it in other populations.This study is supported by the National Institutes of Health (K01-HL120951 to J.M.; and P50-HL105185, P01-AG023394, R01-AG055948 to K.L.T.); and a McLennan Dean’s Challenge Grant Program Award (to J.M.)

A review of maternal prenatal exposures to environmental chemicals and psychosocial stressors-implications for research on perinatal outcomes in the ECHO program.

Exposures to environmental chemicals and psychosocial stressors during pregnancy have been individually associated with adverse perinatal outcomes related to birthweight and gestational age, but are not often considered in combination. We review types of psychosocial stressors and instruments used to assess them and classes of environmental chemical exposures that are known to adversely impact perinatal outcomes, and identify studies relevant studies. We discuss the National Institutes of Healths Environmental influences on Child Health Outcomes (ECHO) program that has combined existing longitudinal cohorts that include more than 50,000 children across the U.S. We describe future opportunities for investigators to use this important new resource for addressing relevant and critical research questions to maternal health. Of the 84 cohorts in ECHO, 38 collected data on environmental chemicals and psychosocial stressors and perinatal outcomes. The diverse ECHO pregnancy cohorts provide capacity to compare regions with distinct place-based environmental and social stressors

How to Assess Trustworthy AI in Practice

This report is a methodological reflection on Z-Inspection. Z-Inspection is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI. This report illustrates for both AI researchers and AI practitioners how the EU HLEG guidelines for trustworthy AI can be applied in practice. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of AI systems in healthcare. We also share key recommendations and practical suggestions on how to ensure a rigorous trustworthy AI assessment throughout the life-cycle of an AI system

A review of maternal prenatal exposures to environmental chemicals and psychosocial stressors-implications for research on perinatal outcomes in the ECHO program.

Exposures to environmental chemicals and psychosocial stressors during pregnancy have been individually associated with adverse perinatal outcomes related to birthweight and gestational age, but are not often considered in combination. We review types of psychosocial stressors and instruments used to assess them and classes of environmental chemical exposures that are known to adversely impact perinatal outcomes, and identify studies relevant studies. We discuss the National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) program that has combined existing longitudinal cohorts that include more than 50,000 children across the U.S. We describe future opportunities for investigators to use this important new resource for addressing relevant and critical research questions to maternal health. Of the 84 cohorts in ECHO, 38 collected data on environmental chemicals and psychosocial stressors and perinatal outcomes. The diverse ECHO pregnancy cohorts provide capacity to compare regions with distinct place-based environmental and social stressors

A review of maternal prenatal exposures to environmental chemicals and psychosocial stressors-implications for research on perinatal outcomes in the ECHO program.

Exposures to environmental chemicals and psychosocial stressors during pregnancy have been individually associated with adverse perinatal outcomes related to birthweight and gestational age, but are not often considered in combination. We review types of psychosocial stressors and instruments used to assess them and classes of environmental chemical exposures that are known to adversely impact perinatal outcomes, and identify studies relevant studies. We discuss the National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) program that has combined existing longitudinal cohorts that include more than 50,000 children across the U.S. We describe future opportunities for investigators to use this important new resource for addressing relevant and critical research questions to maternal health. Of the 84 cohorts in ECHO, 38 collected data on environmental chemicals and psychosocial stressors and perinatal outcomes. The diverse ECHO pregnancy cohorts provide capacity to compare regions with distinct place-based environmental and social stressors