AI in health and medicine

The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world

Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and leverage AI in their operations. The lack of a coherent understanding of how AI technologies create business value, and what type of business value is expected, therefore necessitates a holistic understanding. This study provides a systematic literature review that attempts to explain how organizations can leverage AI technologies in their operations and elucidate the value-generating mechanisms. Our analysis synthesizes the current literature and highlights: (1) the key enablers and inhibitors of AI adoption and use; (2) the typologies of AI use in the organizational setting; and (3) the first- and second-order effects of AI. The paper concludes with an identification of the gaps in the literature and develops a research agenda that identifies areas that need to be addressed by future studies.

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Artificial Intelligence and Business Value: a Literature Review

An empirical characterization of fair machine learning for clinical risk prediction.

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.

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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare.

Research on sex differences in central nervous system disorders has developed substantially in recent years. Here, we discuss selected examples and the implications for drug development. Research on sex differences in central nervous system disorders has developed substantially in recent years. Here, we discuss selected examples and the implications for drug development.

Implications of sex-related differences in central nervous system disorders for drug research and development

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.The online version contains supplementary material available at 10.1007/s13167-022-00284-3. 

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Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach

This chapter describes the current situation regarding sex and gender considerations in clinical trials from preclinical research in animal models to large efficacy trials. Despite women comprise two-thirds of the AD population, for several reasons discussed in this chapter such as eligibility criteria or sociocultural factors, women are underrepresented in clinical trials. We provide a set of recommendations that should help revert this situation.

Simona Mellino

Papers

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare.

Implications of sex-related differences in central nervous system disorders for drug research and development

Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach

Sex and gender considerations in clinical trials for Alzheimer’s disease: Current state and recommendations