Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

Lee, DonHee; Yoon, Seong No

doi:10.3390/ijerph18010271

Cited by 503 publications

(242 citation statements)

References 40 publications

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“…Artificial intelligence (AI) can be defined as the simulation of human intelligence in machines, such as computers and robots, which are programmed to mimic human cognitive functions such as learning and problem-solving [169]. Machine learning (ML) is a subset of AI that uses algorithms that learn from training data before predicting outcomes for new real-world data [169,170].…”

Section: Machine Learning Definition Of Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Psychiatry in the Digital Age: A Blessing or a Curse?

Roth

Papassotiropoulos

Brühl

et al. 2021

IJERPH

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Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor–patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.

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Section: Machine Learning Definition Of Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Psychiatry in the Digital Age: A Blessing or a Curse?

Roth

Papassotiropoulos

Brühl

et al. 2021

IJERPH

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show abstract

“…As an example, when a person is involved in an automobile accident the law currently holds the driver to the " [1]. For other areas, including health care, the use of AI can lead to concerns about privacy, cyber security, data integrity, data ownership, errors, system failures, and control of data so that it is shared to appropriate units but restricted from inappropriate units [48]. -What is the moral status of the algorithms: according to Bostrom and Yudkowsky [10], when AI algorithms take on cognitive work with cognitive tasks previously performed by humans, the AI algorithm inherits the social requirements.…”

Section: Ethics Of the Artificial Intelligence (Ai) Technologymentioning

confidence: 99%

Converged AI, IoT, and blockchain technologies: a conceptual ethics framework

Nehme¹,

Sibai

Abdo

et al. 2021

AI Ethics

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The convergence of AI, IoT, and blockchain as the next disruptive technology is inevitable. Studying the ethics of these converged technologies is crucial for the steady development and adoption of the applications. Even though this topic is crucial, it is still not yet touched in the literature. This work is the first attempt to tackle this issue by developing a conceptual ethics framework for converged AI, IoT, and blockchain. The framework is based on a systematic literature review and categorizes the ethical issues into three layers namely micro (ethical issue involving the technology itself), meso (ethical issue involving the applications), and macro (ethical issue involving the society and institutions).

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“…In addition, with the use of big data and the development of AI technology, the application possibilities in the field are endless, and the application of digital and AI technology is expected to create a new market in the medical information/system field, where the manual system still dominates [ 2 ]. Through this, effective patient health tracking based on technology and wearable devices, reduction of false diagnosis rates by medical staff, and cost savings for medical institutions and pharmaceutical companies have been reported [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

Lee¹,

Youm

2021

Sensors

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As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited opportunity for medication monitoring is a major problem from the perspective of health professionals. The availability of prompt medication error reports can enable health professionals to provide immediate interventions for patients. Furthermore, it can enable clinical researchers to modify experiments easily and predict health levels based on medication compliance. This study proposes a method in which videos of patients taking medications are recorded using a camera image sensor integrated into a wearable device. The collected data are used as a training dataset based on applying the latest convolutional neural network (CNN) technique. As for an artificial intelligence (AI) algorithm to analyze the medication behavior, we constructed an object detection model (Model 1) using the faster region-based CNN technique and a second model that uses the combined feature values to perform action recognition (Model 2). Moreover, 50,000 image data were collected from 89 participants, and labeling was performed on different data categories to train the algorithm. The experimental combination of the object detection model (Model 1) and action recognition model (Model 2) was newly developed, and the accuracy was 92.7%, which is significantly high for medication behavior recognition. This study is expected to enable rapid intervention for providers seeking to treat patients through rapid reporting of drug errors.

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Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

Cited by 503 publications

References 40 publications

Psychiatry in the Digital Age: A Blessing or a Curse?

Psychiatry in the Digital Age: A Blessing or a Curse?

Converged AI, IoT, and blockchain technologies: a conceptual ethics framework

Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

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