Regulatory oversight and ethical concerns surrounding software as medical device (SaMD) and digital twin technology in healthcare

Lal, Amos; Dang, Johnny; Nabzdyk, Christoph S.; Gajić, Ognjen; Herasevich, Vitaly

doi:10.21037/atm-22-4203

Cited by 12 publications

(1 citation statement)

References 37 publications

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“…With the rapid expansion of AI in healthcare, regulatory mechanisms remain limited in their scope especially when it comes to ML applications. 62 …”

Section: Introductionmentioning

confidence: 99%

Acute on chronic liver failure: prognostic models and artificial intelligence applications

Gary

Lal

Simonetto

et al. 2023

Hepatology Communications

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Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.

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“…With the rapid expansion of AI in healthcare, regulatory mechanisms remain limited in their scope especially when it comes to ML applications. 62 …”

Section: Introductionmentioning

confidence: 99%