The role of artificial intelligence in healthcare: a structured literature review

Secinaro, Silvana; Calandra, Davide; Secinaro, Aurelio; Muthurangu, Vivek; Biancone, Paolo

doi:10.1186/s12911-021-01488-9

Cited by 308 publications

(83 citation statements)

References 83 publications

(167 reference statements)

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“…ML has gained wide applicability to develop sophisticated tools in various areas of data processing, such as images, natural language processing, data mining, gaming, robotics, and big data in general ( 6 ). In the past few years, the applications of AI and ML in the healthcare sector have shown ever-increasing growth owing to the rapid progress made possible by deep ML ( 7 ).…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

et al. 2021

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Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.

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Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

et al. 2021

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“…[9][10][11][12]. Artificial intelligence has created a lot of positive impacts in clinical decision making, diagnosis, predictive medicine, etc., which is a good sign for developing personalized systems [13].…”

Section: Introductionmentioning

confidence: 96%

Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages

2021

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With the development of mobile and wearable devices with biosensors, various healthcare services in our life have been recently introduced. A significant issue that arises supports the smart interface among bio-signals developed by different vendors and different languages. Despite its importance for convenient and effective development, however, it has been nearly unexplored. This paper focuses on the smart interface format among bio-signal data processing and mining algorithms implemented by different languages. We designed and implemented an advanced software structure where analysis algorithms implemented by different languages and tools would seem to work in one common environment, overcoming different developing language barriers. By presenting our design in this paper, we hope there will be much more chances for higher service-oriented developments utilizing bio-signals in the future.

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“…30 Increasing access to health data in the private sector powers increasingly capable algorithmic systems. Today we see algorithms being tested in the diagnosis of certain diseases, suggestion and analysis of treatments, review of medical literature 31 , predicting protein folding and genetic makeup 32 , as well as predictions of healthcare costs. 33 In a landscape where healthcare costs are one of the highest expenses for both patients and insurance companies, it is no surprise that both hospitals and insurance companies try to make the most out of the data at hand.…”

Section: Introductionmentioning

confidence: 99%

Case Study: The Distilling of a Biased Algorithmic Decision System through a Business Lens

Hickok¹,

Dorsey²,

O’Brien³

et al. 2022

Preprint

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Technological advances embedded within algorithmic decision systems are being deployed every day. Some of these investments are unquestionably worthwhile, while some others prioritize commercialization of technology ahead of societal impact. The article uses a real-world case from the healthcare sector to demonstrate the design and governance shortfalls of an algorithmic tool through its lifecycle. The healthcare sector sits on a mine of data, making it one of the most lucrative fields for big data–based analytics. However, the remunerative healthcare sector is particularly sensitive to the quality of data and algorithmic design decisions, making it paramount for all stakeholders to safely and ethically develop, deploy and implement the algorithmic tools. Otherwise, these systems can have detrimental effects on the life, well-being, and safety of patients. The authors provide guidance on responsible and sustainable deployment practices applicable across industries. The systematic dissection of this case can be applied to different systems across different domains like employment, credit scoring, housing, education, criminal justice, and many others. Every business automating and streamlining processes and core functions through digital transformation needs to implement new accountability and governance mechanisms and invest in the inevitable culture change necessary.

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The role of artificial intelligence in healthcare: a structured literature review

Cited by 308 publications

References 83 publications

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages

Case Study: The Distilling of a Biased Algorithmic Decision System through a Business Lens

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