Brief History of Artificial Intelligence

Muthukrishnan, N. Moorthy; Maleki, Farhad; Ovens, Katie; Reinhold, Caroline; Forghani, Behzad; Forghani, Reza

doi:10.1016/j.nic.2020.07.004

Cited by 98 publications

(47 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our results clarify several potential sources of patient concern about applications of AI in healthcare and highlight patients’ desire for physician-led oversight of these technologies. If this expectation is not met, it is possible that we could see a third “AI Winter” in which fears of patient harm lead to widespread rejection of healthcare AI by patients and their providers 9 . To avoid that possibility, it is critical that AI developers engage the public in dialogue about both the potential benefits and harms of applications of AI in healthcare 28 .…”

Section: Discussionmentioning

confidence: 99%

“…Studies of nonmedical applications of AI have shown that the public tends to view nonmedical AI in highly variable ways 6 , with factors such as media coverage and early experiences playing key roles in shaping public opinion. These considerations highlight the importance of patient engagement to ensure that these technologies are integrated into healthcare in a manner that fosters public trust 7 , 8 and mitigates widespread patient concerns that might result in another “AI Winter” 9 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Patient apprehensions about the use of artificial intelligence in healthcare

et al. 2021

View full text Add to dashboard Cite

While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Patient apprehensions about the use of artificial intelligence in healthcare

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) is a subset of AI techniques that utilize algorithms that evolve as new data are introduced. Deep learning (DL) is a subclass of ML-based on neural networks, applying a large number of layers, and allowing more complex classification processes [1][2][3][4][5].…”

Section: Methods Of Examinationmentioning

confidence: 99%

“…Discrimination between different types of brain tumors is problematic at imaging. Accurate diagnosis is crucial for planning of treatment to improve patient's outcome, helpful in the grading of tumors and response after therapy [1][2][3][4][5][6][7]. Brain tumor biopsy is considered the gold standard for diagnosis.…”

Section: Brain Tumorsmentioning

confidence: 99%

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

et al. 2021

View full text Add to dashboard Cite

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient’s prognoses.

show abstract

“…Computers executing automated functions were first described in 1950, with the first publication in 1943. Since then, Artificial Intelligence capacity has evolved into deep learning and neural networks, technologies that could simulate interconnected neurons and provide outputs after multiple information layers [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Diabetic retinopathy classification for supervised machine learning algorithms

et al. 2022

View full text Add to dashboard Cite

Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. Main body In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. Conclusion Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.

show abstract

Brief History of Artificial Intelligence

Cited by 98 publications

References 16 publications

Patient apprehensions about the use of artificial intelligence in healthcare

Patient apprehensions about the use of artificial intelligence in healthcare

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Diabetic retinopathy classification for supervised machine learning algorithms

Contact Info

Product

Resources

About