Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

Abstract

Background Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice. Objective The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice. Methods We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings. Results We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation. Conclusions This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

“…The traditional paradigm of regulation of medical devices was not designed for adaptive AI technologies, resulting in inadequate high-quality evidence to support clinical implementation. 44,45 Several of the included studies affirmed the fragmented translation. They accentuated how AI implementation requires organizational development, 34 how evidence about longterm costs and benefits is incomplete, 21 and that they were the first to conduct a HEE in their field.…”

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

“…While this is all well and good, there are times and situations where the immune system cannot differentiate between self and pathogens. When this happens, the immune system attacks the body itself, called an autoimmune disorder [ 39 ]. Autoimmune disorders can be divided into two categories: the primary autoimmune disorder like diabetes type I, which develops at birth and secondary immune disorders like rheumatoid arthritis, which develop later on in life [ 54 ].…”

Perspective of Information Technology Decision Makers on Factors Influencing Adoption and Implementation of Artificial Intelligence Technologies in 40 German Hospitals: Descriptive Analysis

“…In recent years, artificial intelligence (AI) in medicine has gained significant attention, with innovative technologies promising better quality of diagnosis [ 1 - 3 ], treatment [ 1 ], advancements in personalized medicine [ 1 , 4 ], and improvements in workflow [ 5 ]. Simultaneously, these technologies have the potential to save time and cost [ 1 , 6 ].…”

“…A recent systematic review by Yin et al [ 5 ] demonstrated hesitancy and slow uptake of AI technologies. The authors reported on real-life implementations of AI in health care.…”

“…The most common applications of AI tools were in the field of decision support. These technologies mainly focus on specific diseases such as sepsis, breast cancer, and diabetic retinopathy [ 5 ]. Diverging outcome measures and low-quality studies were prevalent in the review, making it difficult for decision makers to compare and evaluate AI effectiveness, advantages, and disadvantages.…”