ORCID Profile
0000-0002-5923-6857
Current Organisation
Deakin University
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Publisher: Oxford University Press (OUP)
Date: 19-12-2020
Abstract: Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes 2 contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 02-2022
DOI: 10.1016/J.ARTMED.2021.102158
Abstract: Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.
Publisher: IEEE
Date: 11-2016
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2022
DOI: 10.1101/2022.12.05.22283129
Abstract: Meta-analytic evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI) driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multi-arm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. The Vibe Up study is a pragmatically-oriented, decentralised AI-adaptive group sequential randomised controlled trial (RCT) comparing the effectiveness of one of three brief, two week digital self-guided interventions (mindfulness, physical activity, or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (DASS-21 total score) from pre-intervention to post-intervention. Secondary outcomes include change in depression, anxiety, and stress (measured by DASS-21 subscales) from pre-intervention to post-intervention. Planned contrasts will compare the four groups (i.e., the three intervention and control) using self-reported psychological distress at pre-specified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice [1] was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). The trial is registered with the Australian New Zealand Clinical Trials Registry (AC-TRN12621001223820). The study addresses an important clinical question using novel, advanced methods The trial uses short-duration interventions designed to improve coping responses to transient stressors, which addresses the most common needs of university students A value of information analysis is included to compare the value of the new trial methods with traditionalapproaches Digital phenotyping is used to explore smartphone sensor information with clinical outcomes More than 12 mini-trials might be required to determine the ranking for the interventions The interventions may prove to be of the same level of effectiveness for each level of severity Interventions other than those examined in this study, such as CBT, may be more effective and remain untested The methodology assumes that the three digital interventions are configured to deliver similar doses and/or have approximate fidelity with standard methods
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 03-2021
No related grants have been discovered for Manisha Senadeera.