Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology

By Jenna Wiens, Erica S Shenoy. Clinical Infectious Diseases, 2018

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Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, methodology): “Machine learning (ML) is an AI approach that involves various methods of enabling an algorithm to learn [27, 29 (cid:129) , 31 – 35].”
Trust in AI: progress, challenges, and future directions (2024, unknown): “…achieved signi ﬁ cant progress in out-performing conventional solutions in many areas, including health (Itani et al., 2019; Shailaja et al., 2018; Wiens and Shenoy, 2018), autonomous transportation (Qayyum et al., 2020; Schwarting et al., 2018; Sligar, 2020), military (Galán et al., 2022;…”
Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, methodology): “These datasets enable the use of advanced data analysis techniques based on machine learning (ML) to extract vast amounts of knowledge that can improve various aspects of healthcare [2].”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “These issues have led researchers to argue that machine learning must be ‘interpretable by design’ [1] and that transparency is essential for the adoption of intelligent systems, e.g. for medical diagnoses [31,80].”
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians (2019, background): “Existing literature in the machine learning field provides relatively successful methods to train such mathematical models that learn useful knowledge from data [9-11].”