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2 results

  • 10 - Labeling of Direct-to-Consumer Medical Artificial Intelligence Applications for “Self-Diagnosis”

  • from Part III - The Shape of the Elephant for Digital Home Diagnostics
  • Edited by I. Glenn Cohen, Harvard Law School, Massachusetts, Daniel B. Kramer, Harvard Medical School, Massachusetts, Julia Adler-Milstein, University of California, San Francisco, Carmel Shachar, Harvard Law School, Massachusetts
  • Book:
    Digital Health Care outside of Traditional Clinical Settings
    Published online:
    25 April 2024
    Print publication:
    02 May 2024, pp 139-155
    • Chapter
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  • Summary

    Artificial intelligence (AI) is changing our daily life and the way we receive health care. For example, Google hopes to soon start a pilot study for its “AI-powered dermatology tool,” an app with knowledge of 288 skin conditions. The FDA has also already permitted the marketing of similar medical devices, such as Apple’s electrocardiogram (ECG) app. Interestingly, both Google and Apple advertise their direct-to-patient/consumer (DTP/DTC) apps as information tools only that are not intended to provide a diagnosis. This is due to their “over-the-counter” nature, although Apple’s clinical study of the ECG app, for example, correctly diagnosed atrial fibrillation with 98.3 percent sensitivity and 99.6 percent specificity. But do patients and consumers really understand that such and similar medical apps do not replace traditional diagnosis and treatment methods? Moreover, many DTP/DTC medical AI apps for “self-diagnosis” are opaque (“black boxes”), can continuously learn, and are vulnerable to biases. Patients and consumers need to understand the indications for use, the model characteristics, and the risks and limitations of such tools. However, the FDA has not yet developed any labeling standards specifically for AI-based medical devices, let alone for those directly addressed to patients/consumers. This chapter explores not only the benefits of labeling, such as helping patients and consumers to make more informed decisions, but also the potential limitations. It also makes suggestions on the content of labeling for DTP/DTC AI diagnosis apps. In particular, this chapter argues that the advertisement of this technology as “information tools only” rather than “diagnosis tools” is misleading for consumers and patients.

  • Seasonal influenza among children diagnosed by their guardians: a small pilot study in Japan

  • Hiroki Maita, Tadashi Kobayashi, Hiroshi Osawa, Hiroyuki Kato
  • Journal:
    Primary Health Care Research & Development / Volume 20 / 2019
    Published online by Cambridge University Press:
    15 April 2018, e55
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  • Aim

    We aimed to elucidate the accuracy and optimal cut-off point of the self-diagnosis of influenza and the associated clinical symptoms of children by their guardians, compared with those of the rapid influenza diagnostic test (RIDT).

    Background

    Seasonal influenza is a common outpatient problem during the winter season. A paediatric influenza epidemic has socio-economic impacts like temporary school closure, school event cancellations, and unscheduled work absences among parents. Hence, early identification and assessment of influenza to prevent its spread is important from a societal perspective.

    Method

    We performed a cross-sectional observational study in a rural clinic in Japan every winter season from December 2013 to March 2016. We retrospectively extracted information from the medical records and pre-examination checklists of 24 patients aged <12 years (mean age, 5.4 years; men, 54.2%). The data extracted from the medical records and pre-examination checklist included the baseline characteristics (age, sex and past medical history of influenza), clinical signs and symptoms, diagnosis by guardians (%) and RIDT results.

    Findings

    The optimal cut-off point of the self-diagnosis of influenza by guardians was 80%, with a sensitivity and specificity of 63.6% (95% confidence interval: 30.8–89.1) and 92.3% (64.0–99.8). At a 50% cut-off point, the sensitivity and specificity were 90.9% (58.7–99.8) and 53.8% (25.1−80.8). The accuracy of feeling severely sick, as estimated by the guardians showed a sensitivity and specificity of 90.9% (58.7–99.8) and 69.2% (38.6–90.9). Our study indicates that the diagnosis of seasonal influenza by guardians to their children would be useful in the establishment of both confirmatory diagnoses when it has high probability above the optimal cut-off point (80%), and exclusion diagnosis when it has low probability (50%). Not feeling severely sick, estimated by the guardians might be a useful indicator for the exclusion of paediatric influenza.