Artificial Intelligence Transforms the Future of Health Care

Noorbakhsh-Sabet, Nariman; Zand, Ramin; Zhang, Yanfei; Abedi, Vida

doi:10.1016/j.amjmed.2019.01.017

Cited by 334 publications

(249 citation statements)

References 62 publications

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“…Deep learning has been widely used for clinical information extraction, phenotype discovery, image analysis, and next generation sequencing [72], [73]. AI upsurges learning abilities and offers decision-making capabilities at a scale to transform the healthcare future [74]. Therefore, physicians in everyday practice get pressure to look around innovations spreading over faster than ever through the use of disruptive technologies and exponential growth of healthcare data-the "Big data" [75].…”

Section: Big Data and Artificial Intelligencementioning

confidence: 99%

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Precision Medicine Informatics: Principles, Prospects, and Challenges

et al. 2020

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Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.

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Section: Big Data and Artificial Intelligencementioning

confidence: 99%

“…For example, a simple sensing system can detect whether the dose or plasma concentration has risen above the therapeutic range. Such a concept of an IoT-based ADR is found in [74]. The work makes use of barcode or NFC-enabled devices so that the patient side recognizes the drugs.…”

Section: A Risk Minimization In Adrmentioning

confidence: 99%

Precision Medicine Informatics: Principles, Prospects, and Challenges

et al. 2020

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“…Recently, artificial intelligence (AI) has risen as a promising alternative. Built based on the biological networks of the human brain [8], AI covers a wide range of technologies that are capable of performing cognitive functions by mimicking human intelligence [9]. While promising results in other fields (e.g., engineering, business, and everyday applications) have been shown, increasing efforts are being made to incorporate AI into healthcare settings [10,11].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

Song

Kim

Bong

et al. 2019

J Korean Acad Child Adolesc Psychiatry

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Objectives:The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

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“…The areas where artificial intelligence (AI) can be transformative is beyond what we summarized. 1 It is important to mention that the field of AI has been especially transformative in areas where clinical imaging is well integrated into the clinical practice, areas such as radiology, pathology, cancer, and surgery.The AI transformation is mainly a result of the push from major high-tech companies and has strong roots in the gaming industry, face recognition, personalized advertising, and more recently, in traffic monitoring systems and autonomous driving among others. Machine learning, and more specifically, deep-learning models are designed for use in imaging applications and have more recently been used in other areas as well, including data augmentation to improve access to otherwise constrained, 2 or limited, 3,4 clinical data.…”

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confidence: 99%

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Abedi

Zand

2020

The American Journal of Medicine

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We agree with the comments in the letter to the editor from Norris and Raza. The areas where artificial intelligence (AI) can be transformative is beyond what we summarized. 1 It is important to mention that the field of AI has been especially transformative in areas where clinical imaging is well integrated into the clinical practice, areas such as radiology, pathology, cancer, and surgery.The AI transformation is mainly a result of the push from major high-tech companies and has strong roots in the gaming industry, face recognition, personalized advertising, and more recently, in traffic monitoring systems and autonomous driving among others. Machine learning, and more specifically, deep-learning models are designed for use in imaging applications and have more recently been used in other areas as well, including data augmentation to improve access to otherwise constrained, 2 or limited, 3,4 clinical data. Today's high hope to use machine learning in the medical field is driven by 1) large repositories of digitalized images; 2) easier access to connect various data modality to complement insight gained from imaging and clinical features of the patient; 3) prospect of availability of ultrafast 5G connectivity; and 4) advanced computing infrastructures (such as federated computing, fog computing, etc.), platforms, and standards. Furthermore, augmented reality, another application of AI, which also uses data from wearables sensors, could have a key role in advancing the field of surgery, medical education, mental health, and addiction, etc. However, unlike nonclinical applications of machine learningfor instance, gaming-reaching the most clinically valuable results, such as diagnostic accuracy, is more important than understanding the logic behind the algorithm's decision.As it was highlighted in the letter to the Editor, if intelligent systems can one day outperform expert pathologists or radiologists, then the future for digital histology will be different. However, we would like to think that if we do it right, technology can be a tool to assist physicians through a digital deliberation, giving them back their valuable patient-physician time and helping them take better care of their patients by focusing on the patients rather than the computer screen. And maybe when we think of our next generation of physicians, we may want to invest in training programs where the focus is on the comprehensive wellbeing of the patients. Maybe we should invest in promoting a field of medicine that confronts the transformational challenges that are in part due to the digitalization of health care, paving the way to more successful adoption of novel technologically driven solutions.

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Artificial Intelligence Transforms the Future of Health Care

Cited by 334 publications

References 62 publications

Precision Medicine Informatics: Principles, Prospects, and Challenges

Precision Medicine Informatics: Principles, Prospects, and Challenges

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

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