From a comprehensive and systematic search of the relevant literature on signal data signature (SDS)-based artificial intelligence/machine learning (AI/ML) systems designed to aid in the diagnosis of COVID-19 illness, we aimed to reproduce the reported systems and to derive a performance goal for comparison to our own medical device with the same intended use. These objectives were in line with a pathway to regulatory approval of such devices, as well as to acceptance of this unfamiliar technology by disaster/pandemic decision makers and clinicians. To our surprise, none of the peer-reviewed articles or pre-print server records contained details sufficient to meet the planned objectives. Information amassed from the full review of more than 60 publications, however, did underscore discrete impediments to bringing AI/ML diagnostic solutions to the bedside during a pandemic. These challenges then were explored by the authors via a gap analysis and specific remedies were proposed for bringing AI/ML technologies in closer alignment with the needs of a Total Product Life Cycle (TPLC) regulatory approach.
AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Results and Challenges of a Systematic Review of the Relevant Literature