Abstract:AbstractArtificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-… Show more
“…Sharing of de-identified datasets will further maximise breakthrough opportunities in research. We have commented elsewhere [ 5 ] on the role of machine learning/AI in the analysis of such datasets, and in the health sector more broadly, and do not consider this issue further here. Fig.…”
Section: The Australian Epilepsy Projectmentioning
Highlights
TeleNP represents a medium through which clinical research and important clinical care can continue.
We discuss benefits, challenges, and practical considerations in the use of teleNP.
We describe how the Australian Epilepsy Project has adapted to the COVID-19 environment.
TeleNP data acquired to date reveals deficits typical of those observed in epilepsy.
TeleNP presents an opportunity to expand the reach and breadth of neuropsychological services.
“…Sharing of de-identified datasets will further maximise breakthrough opportunities in research. We have commented elsewhere [ 5 ] on the role of machine learning/AI in the analysis of such datasets, and in the health sector more broadly, and do not consider this issue further here. Fig.…”
Section: The Australian Epilepsy Projectmentioning
Highlights
TeleNP represents a medium through which clinical research and important clinical care can continue.
We discuss benefits, challenges, and practical considerations in the use of teleNP.
We describe how the Australian Epilepsy Project has adapted to the COVID-19 environment.
TeleNP data acquired to date reveals deficits typical of those observed in epilepsy.
TeleNP presents an opportunity to expand the reach and breadth of neuropsychological services.
“… 52 As an exemplar, in neurology, multimodal data—high-dimensional brain imaging and genetics—can be used in a deep-learning model for improved prediction of epilepsy. 53 Another example is its use in anesthesiology where it can be used to improve the depth of anesthesia monitoring, pain management, and prediction of operative, postoperative, and critical care–related events. 54 Furthermore, in cardiovascular medicine, it can be used in the management of heart failure, including identification of patients at risk, development of risk assessment tools, and the use of multimodal EHR data for clinical decision support in these patients.…”
Section: Future Directions and Role Of Ai In Perinatal Healthmentioning
Advances in public health and medical care have enabled better pregnancy and birth outcomes. The rates of perinatal health indicators such as maternal mortality and morbidity; fetal, neonatal, and infant mortality; low birthweight; and preterm birth have reduced over time. However, they are still a public health concern, and considerable disparities exist within and between countries. For perinatal researchers who are engaged in unraveling the tangled web of causation for maternal and child health outcomes and for clinicians involved in the care of pregnant women and infants, artificial intelligence offers novel approaches to prediction modeling, diagnosis, early detection, and monitoring in perinatal health. Machine learning, a commonly used artificial intelligence method, has been used to predict preterm birth, birthweight, preeclampsia, mortality, hypertensive disorders, and postpartum depression. Real-time electronic health recording and predictive modeling using artificial intelligence have found early success in fetal monitoring and monitoring of women with gestational diabetes especially in low-resource settings. Artificial intelligence–based methodologies have the potential to improve prenatal diagnosis of birth defects and outcomes in assisted reproductive technology too. In this scenario, we envision artificial intelligence for perinatal research to be based on three goals: (1) availability of population-representative, routine clinical data (rich multimodal data of large sample size) for perinatal research; (2) modification and application of current state-of-the-art artificial intelligence for prediction and classification in health care research to the field of perinatal health; and (3) development of methods for explaining the decision-making processes of artificial intelligence models for perinatal health indicators. Achieving these three goals via a multidisciplinary approach to the development of artificial intelligence tools will enable trust in these tools and advance research, clinical practice, and policies to ensure optimal perinatal health.
“…Neurology clinical decision support (Pedersen et al, 2020) prognosis of neurological disorders (Patel, 2021) precision psychiatry/ specificity (Bzdok et al, 2018) neuroimaging biomarkers (Bernstein et al, 2018) Oncology precision oncology diagnostic tools (Bera et al, 2019) radiomics/ biomarker models (Forghani et al, 2019) cancer genomics/ precision medicine Fields such as neurology, oncology, cardiology and genetics are four specific branches of medicine that use artificial intelligence (Yu et al, 2018) (Figure 1); health applications can range from context specific (Madanian et al, 2018(Madanian et al, , 2019 to general well-being solutions (Airehrour et al, 2020). AI is particularly useful in certain aspects of healthcare, yet less appropriate in others.…”
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