The impact of machine learning on patient care: A systematic review

Improving the quality of machine learning in health applications and clinical research

“…Reporting guidelines and MLH frameworks represent a key step in bridging these two (often) separate worlds. ❐ Bilal A. Mateen 1,2 , James Liley 1,3 , Alastair K. Denniston 4,5 , Chris C. Holmes 1,6 and Sebastian J. Vollmer 1,7 ✉ comment Published online: 2 October 2020 https://doi.org/10.1038/s42256-020-00239-1…”

“…The potential of MLH is vast, with demonstrations of ML-based tools being able to achieve human-level or above diagnostic and prognostic capabilities having been described in almost every clinical specialty 5 . However, the number of ML tools adopted in clinical applications reflects only a fraction of the investment into the field as a whole, suggesting that most applications of MLH have not progressed beyond the initial publication 6 . On closer examination, there appears to be a tendency for ML researchers to stop once adequately accurate prediction (and hence novelty) has been demonstrated 6 with translation into the clinical practice left to interested domain experts 7 .…”

“…However, the number of ML tools adopted in clinical applications reflects only a fraction of the investment into the field as a whole, suggesting that most applications of MLH have not progressed beyond the initial publication 6 . On closer examination, there appears to be a tendency for ML researchers to stop once adequately accurate prediction (and hence novelty) has been demonstrated 6 with translation into the clinical practice left to interested domain experts 7 . This translation is difficult, with a high failure rate 6,8 , but some of the potential difficulties may be readily addressable.…”

“…On closer examination, there appears to be a tendency for ML researchers to stop once adequately accurate prediction (and hence novelty) has been demonstrated 6 with translation into the clinical practice left to interested domain experts 7 . This translation is difficult, with a high failure rate 6,8 , but some of the potential difficulties may be readily addressable. Below we explore how the use of clinical reporting guidelines (and research frameworks more generally) can facilitate better translation and more clinically useful applications of MLH.…”

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

“…Machine learning has the ability to serve the work of clinicians by processing and organization huge pile of patient data stored in electronic health records and is implemented in clinical applications which include identifying patients having high risk who needs ICU (Escobar et al, 2016), detecting early symptoms leading to lung cancer (Ardila et al, 2019), determining the respiratory condition of the patient from chest X-rays (Rajpurkar et al, 2017). Thus, AI and ML enhances the performance of diagnosis, prognosis and also in management decisions in healthcare domain (Ben-Israel et al, 2019). Also, deep-learning techniques have a significant impact on the state-of-art speech recognition (Hinton et al, 2012) and visual recognition techniques (Krizhevsky et al, 2017) which has a potentially bigger role in future.…”