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Regulations for the development of deep technology applications in healthcare urgently needed to prevent abuse of vulnerable patients
  1. Dinesh Visva Gunasekeran
    1. National University of Singapore, Singapore
    1. Correspondence to Dr Dinesh Visva Gunasekeran, National University of Singapore, Singapore 119077; dineshvg{at}

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    The article ‘First compute no harm’ contributed by Coiera et al1 adds to the exciting discussion about the immense potential of deep technology such as machine learning to positively transform healthcare.2 This article highlights the growing chasm between today’s innovation and the scope of existing regulations. This is a problem most aggravated in low-income and middle-income countries.3 Development has begun, although the inauguration of holistic regulatory frameworks is lagging. This begets additional concerns about improper conduct of testing,4 manipulation that is difficult to detect without stringent data reporting5 and ethical implications of care recommendations based on probabilistic analyses.6

    ‘Machine learning’ was initially defined as a ‘field of study that gives computers the ability to learn without being explicitly programmed’, and later refined by Tom Mitchell (1997)7 as a program that learns ‘from an experience (E) with respect to some task (T) and some performance measure (P), if its performance on T, as measured by P, improves with experience E’. Machine learning has given rise to many stellar applications in the processing of biological information …

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    • Contributors The authors both contributed to the concepts in this editorial following a panel discussion at the NUS domain expert presentation on Fairness, Accountability and Transparency in Artificial Intelligence.

    • Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests DVG reports advisory to university-affiliated technology developers/start-ups in Singapore, as well as the Collaborative Ocular Tuberculosis Study (COTS) group. COTS is an international initiative to use big data to better understand the elusive ocular tuberculosis (TB), an early opportunity to address asymptomatic carriage of TB infection.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Collaborators Emmanuel Maroye.