The challenge of clinical adoption—the insurmountable obstacle that will stop machine learning?

Abstract

Machine learning promises much in the field of radiology, both in terms of software that can directly analyse patient data and algorithms that can automatically perform other processes in the reporting pipeline. However, clinical practice remains largely untouched by such technology. This article highlights what we consider to be the major obstacles to widespread clinical adoption of machine learning software, namely: representative data and evidence, regulations, health economics, heterogeneity of the clinical environment and support and promotion. We argue that these issues are currently so substantial that machine learning will struggle to find acceptance beyond the narrow group of applications where the potential benefits are readily evident. In order that machine learning can fulfil its potential in radiology, a radical new approach is needed, where significant resources are directed at reducing impediments to translation rather than always being focused solely on development of the technology itself.

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

“…Although the predicted accuracy attained by various ML techniques proposed in various automated PD detection studies was good, the adoption of the ML framework with cloud computing and edge computing in medical healthcare is presently not supported [ 110 , 111 ]. Neurologists and other medical professionals do not feel confident using these technologies to diagnose Parkinson’s disease in their current state.…”

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

“…Despite the high prediction accuracy obtained by many deep learning models proposed in various automated PD detection studies, the adoption of the deep learning model as a CAD tool in healthcare is currently not supported [21,22]. In their current form, neither neurologists nor other healthcare workers are comfortable to rely on CAD tools to diagnose the PD.…”

“…Also, the selection of the most relevant features must be carried out by an experienced expert system that is knowledgeable in terms of various feature selection tools [15,16]. This has led to the somewhat poor adoption of machine learning models as the future CAD tools as feature extraction and selection can be complicated procedures comprehensible by machine learning experts, but not so by the end-user of the CAD tool [21,22]. Such end-users may involve healthcare experts such as practicing clinicians, health researchers, or other domain applications.…”

“…Deep learning models are also known as the 'black box' so it is almost impossible to clearly understand the mechanisms behind a deep learning model when it makes a given prediction [22,23]. Despite achieving high prediction accuracy, end-users of the CAD tools (e.g., neurologists and healthcare workers) cannot make a diagnosis without sufficient evidence, and this evidence is not currently provided by deep learning models [21,23]. Hence, neurologists are not able to trust the CAD tools as they cannot afford to make a diagnosis without concrete evidence, explainability and interpretability of the somewhat black box style method used to produce an outcome.…”

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

“…The future potential for automated approaches to make it into clinical use also require addressing a number of systemic challenges, including: (1) Improving access to large scale, expertly annotated datasets to train and test techniques on data that is representative of real world scenarios; (2) Evidence that techniques are robust and reliable enough to enable clinical use and provide sufficient incremental value to justify the associated costs (i.e., health economic analysis); (3) Regulations surrounding the updates of medical technology could inhibit the rapid adoption required for AI in clinical scenarios; (4) Data ownership could impact how techniques develop, particularly if research techniques develop with large scale datasets to the point of commercial potential. [ 235 ]. These are both multi-disciplinary challenges and opportunities for the engineering, computer science and medical research fields.…”