1] and social care for an entire population through preventive strategies, protection from disease, promotion of healthy lifestyles, and population screening through knowledge capture (typically in the form of big data). Overall governance will offer a patient-centred approach with the consideration of patient advocacy and workforce and resource management [2] (Fig 1). Herein, we will break down the role of ML in each of these areas.
https://doi.org/10.1371/journal.pmed.1002692.g001
3]. Here, the major limitation is access to large and high-quality population-level datasets with which to apply ML approaches. We feel that the unification of the United Kingdom National Health Service (NHS) dataset of over 66 million individuals in the form of EHRs or even patient health records (PHRs; in which patient data accompany the patients directly) can offer one of the largest datasets worldwide for analysis by ML. This could offer improved predictions for clinical outcomes from current records and could also provide novel hypothesis-generating concepts that may lead research to better understand disease behaviours and their treatments.
4] so that strategies to enhance uptake can be implemented. They can also be used to identify the illegal online sale of prescription opioids from global online vendors [5]. Environmental data from climate sensors can predict climate crises, ecosystem shifts, and pollution trends [6] with higher accuracy than current systems to allow preparation for emergency responses to climate situations or support ecological management strategies. Data from city transportation audits or integrated smart city sensors can also be applied to predict locations of injuries or trauma due to car crashes within towns and cities [7] and inform site-specific interventions to prevent urban vehicular accidents.
8]. The information for this can be obtained from EHRs with the consent and national regulatory adherence in order to target unhealthy behaviours such as buying tobacco-based products or the individual purchasing of alcohol- or sugar-based drinks.
9]. This test was utilised to help identify individuals at high risk of colorectal cancer who were noncompliant to a national screening programme; however, this technology may eventually have the capacity to offer full population screening (also allowing for personalised screening). Deep-learning image screening, for example, on mammography is currently being developed and has the potential to enhance health delivery by supporting scalable, cost-effective diagnostic decisions.
10] can result in the reinforcement of historical biases (and therefore discrimination), for example, in declining the opportunity of health insurance to individuals of a particular race/ethnic group or demographic because of an unrepresentative or biased data source. However, if applied judiciously, ML approaches have the potential to assess for disparities or unjustified data discrimination to ensure adherence to accepted guidelines and data health justice across society.
11]. This prospect has initiated a formidable societal controversy, as arguments over the benefits of AI in supporting resource deficits have also been countered with arguments that AI will lead to massive job losses—for example, in diagnostic radiology or pathology, for which an ML algorithm could appraise multitudes of images on a 24-hour work cycle. Many of these issues carry an impact beyond that of healthcare and have a bearing on national and international economic strategy as well as the wider public discourse on the exact role of AI in society. We suggest that AI’s first and least perilous role should be in resourcing healthcare. This will likely disrupt current work practices but will also generate new jobs and roles. More importantly, ML-based technologies may offer society ‘freed-up’ health practitioner time to focus on direct patient care.
12], will have a widespread and pervasive role within our society. Arguably the least explored area of AI in health policy is its role in governance, which ranges from legislation to strategy, financing, and accountability. Here, ML solutions could offer rapidly produced analytics and appraisal of policy statements. Using these, policy makers and politicians can drive the next generation of health policies. Although many of these ML systems remain experimental and theoretical, they could ultimately present the largest transformative role in health governance to date.
Article source: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002692