The Evolving Role of Artificial Intelligence in Gastrointestinal Histopathology: An Update

Codipilly, D. Chamil; Faghani, Shahriar; Hagan, Catherine; Lewis, Jason; Erickson, Bradley J.; Iyer, Prasad G.

doi:10.1016/j.cgh.2023.11.044

Cited by 5 publications

(2 citation statements)

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“…This could have potential applications for the analysis of histopathologic images of joints, kidney biopsies, muscle biopsies, and other organs relevant to Rheumatology practice. 33,34 predIctIon of dIsease course Rheumatic diseases are characterized by flares of disease activity and periods of disease control. The accurate prediction of disease flares is of particular interest to Rheumatologists, as each disease flare potentially damages the joint or other organs.…”

Section: Image Analysis To Aid Diagnosis and Clinical Assessmentmentioning

confidence: 99%

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What does artificial intelligence mean in rheumatology?

Chandwar,

Prasanna Misra

2024

Arch Rheumatol

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Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician’s diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

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Section: Image Analysis To Aid Diagnosis and Clinical Assessmentmentioning

confidence: 99%

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confidence: 99%