Artificial Intelligence (AI) and rheumatology: a potential partnership

Kothari, Suruchi; Gionfrida, Letizia; Bharath, Anil A.; Abraham, Sonya

doi:10.1093/rheumatology/kez194

Cited by 24 publications

(6 citation statements)

References 6 publications

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“…In the past 50 years, clinical practice has been going through a transition fueled by technological developments. Biomed ical engineering, [1][2][3][4][5][6][7][8][9] material science, [10][11][12][13][14][15][16][17][18] and artificial intel ligence (AI) [19][20][21][22][23][24] have been transforming the entire medical landscape: diagnostics, [25][26][27][28][29][30][31][32][33] drug development, 24,[34][35][36] drug delivery, [37][38][39][40][41][42][43][44][45][46] and data analytics. [47][48][49]…”

Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

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Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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“…There are several challenges in rheumatology that could potentially be addressed through the use of AI [ 8 ]. For example, assessment of disease activity in rheumatoid arthritis (RA) usually relies on non-specific blood tests and subjective measures, such as patient self-reporting, whereas AI could be used to identify specific and sensitive disease biomarkers that provide objective measures of change and earlier prediction of disease flairs and treatment non-compliance [ 8 ]. Machine learning methods using electronic health record data have been used to accurately identify patients with RA [ 9 ] and predict complex disease outcomes [ 10 ].…”

Section: Changes In the Way Rheumatologists Practise Medicinementioning

confidence: 99%

Responding to and Driving Change in Rheumatology: Report from the 12th International Immunology Summit 2021

Felten

Rosine

2022

Rheumatol Ther

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The coronavirus disease 2019 (COVID-19) pandemic has accelerated changes to rheumatology daily clinical practice. The main goal of the 12th International Immunology Summit, held 25–26 June, 2021 (virtual meeting), was to provide direction for these active changes rather than undergoing change reactively in order to improve patient outcomes. This review describes and explores the concept of change in rheumatology clinical practice based on presentations from the Immunology Summit. Many of the changes to rheumatology practice brought about by the COVID-19 pandemic may be considered as having a positive impact on disease management and may help with the long-term development of more patient-focused treatment. Rheumatologists can contribute key knowledge regarding the use of immunosuppressive agents in the context of the pandemic, and according to the European League Against Rheumatism, they should be involved in any multidisciplinary COVID-19 guideline committees. New technologies, including telemedicine and artificial intelligence, represent an opportunity for physicians to individualise patient treatment and improve disease management. Despite major advances in the treatment of rheumatic diseases, the efficacy of available disease-modifying anti-rheumatic drugs (DMARDs) remains suboptimal and data regarding serological biomarkers are limited. Synovial tissue biomarkers, such as CD68 + macrophages, have shown promise in elucidating pathogenesis and targeting treatment to the individual patient. In spondyloarthritis (SpA) or psoriatic arthritis (PsA), information regarding the effectiveness of the available agents with different mechanisms of action may be integrated to manage patients using a treat-to-target approach. Early diagnosis of SpA and PsA is important for optimisation of treatment response and long-term outcomes. Improving our understanding of disease pathogenesis and practice methods may help reduce diagnostic delays, thereby optimising disease outcomes in patients with rheumatic diseases.

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“…One of the additional interesting areas that AI has become really beneficial to is that of Rheumatology, more commonly known as the medical field for joint and bone health. While it would seem to be a less extreme scenario compared to that of Oncology, rheumatology typically requires extremely targeted treatments based on clinical manifestation, the vastly different symptoms each patient experiences, the severity of joint pains, previous treatment history, and a few other factors [8]. With a medical model focused around rheumatology, a clinical decision support system would offer an electronic aid in indicating risk progression over time, specific gender or biological based progression traits, protein types building in between joints, or erosions in image recognition [6].…”

Section: Rheumatology and Implementation Of Aimentioning

confidence: 99%

Exploring AI in Healthcare: How the Acceleration of Data Processing can Impact Life Saving Diagnoses

Janes

Bansal

Baron

2022

Electronic Workshops in Computing

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Artificial Intelligence (AI) is one of the biggest topics being discussed in the realm of Computer Science and it has made incredible breakthroughs possible in so many different industries. One of the largest issues with utilizing computational resources in the health industry historically is centered around the quantity of data, the specificity of conditions for accurate results, and the general risks associated with being incorrect in an analysis. Although these all have been major issues in the past, the application of artificial intelligence has opened up an entirely different realm of possibilities because accessing massive amounts of patient data, is essential for generating an extremely accurate model in machine learning (ML). This paper presents an analysis of tools and algorithm design techniques used in recent times to accelerate data processing in the realm of healthcare, but one of the most important discoveries is that the standardization of conditioned data being fed into the models is almost more important than the algorithms or tools being used combined.

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Artificial Intelligence (AI) and rheumatology: a potential partnership

Cited by 24 publications

References 6 publications

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Responding to and Driving Change in Rheumatology: Report from the 12th International Immunology Summit 2021

Exploring AI in Healthcare: How the Acceleration of Data Processing can Impact Life Saving Diagnoses

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