Artificial Intelligence in medicine: today and tomorrow

Briganti, Giovanni; Moine, Olivier Le

doi:10.31219/osf.io/gf24u

Cited by 37 publications

(47 citation statements)

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“…Another intriguing perspective is the application of artificial intelligence (AI) models which allows computational analysis and interpretation of large-scale molecular data generation by exploiting machine learning algorithms and neural networks [ 196 , 197 ]. For example, classifiers like artificial neural networks, support vector machines and Bayesian inference have already been employed in pilot studies to screen KTx recipients requiring renal biopsy [ 198 ] and AI has proved useful to improve estimation of TAC Area Under the Concentration Over Time Curve [ 199 ].…”

Section: Current Limits and Perspectives Of Biomarkers In Renal Trmentioning

confidence: 99%

Recent Advances on Biomarkers of Early and Late Kidney Graft Dysfunction

Quaglia

Merlotti

Guglielmetti

et al. 2020

IJMS

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New biomarkers of early and late graft dysfunction are needed in renal transplant to improve management of complications and prolong graft survival. A wide range of potential diagnostic and prognostic biomarkers, measured in different biological fluids (serum, plasma, urine) and in renal tissues, have been proposed for post-transplant delayed graft function (DGF), acute rejection (AR), and chronic allograft dysfunction (CAD). This review investigates old and new potential biomarkers for each of these clinical domains, seeking to underline their limits and strengths. OMICs technology has allowed identifying many candidate biomarkers, providing diagnostic and prognostic information at very early stages of pathological processes, such as AR. Donor-derived cell-free DNA (ddcfDNA) and extracellular vesicles (EVs) are further promising tools. Although most of these biomarkers still need to be validated in multiple independent cohorts and standardized, they are paving the way for substantial advances, such as the possibility of accurately predicting risk of DGF before graft is implanted, of making a “molecular” diagnosis of subclinical rejection even before histological lesions develop, or of dissecting etiology of CAD. Identification of “immunoquiescent” or even tolerant patients to guide minimization of immunosuppressive therapy is another area of active research. The parallel progress in imaging techniques, bioinformatics, and artificial intelligence (AI) is helping to fully exploit the wealth of information provided by biomarkers, leading to improved disease nosology of old entities such as transplant glomerulopathy. Prospective studies are needed to assess whether introduction of these new sets of biomarkers into clinical practice could actually reduce the need for renal biopsy, integrate traditional tools, and ultimately improve graft survival compared to current management.

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Section: Current Limits and Perspectives Of Biomarkers In Renal Trmentioning

confidence: 99%

Recent Advances on Biomarkers of Early and Late Kidney Graft Dysfunction

Quaglia

Merlotti

Guglielmetti

et al. 2020

IJMS

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“…AI-powered technologies are thriving, and they are currently changing medical practice. AI has surpassed humans in several medical areas, such as disease diagnosis based on medical or pathological images and disease activity monitoring for atrial fibrillation and epilepsy relapse ( 24 – 29 ). Pioneering work of AI applications in anesthesiology has been carried out in several aspects, including anesthesia depth monitoring, control of anesthesia, risk prediction, and logistics management ( 30 ).…”

Section: Ai-pca: a Promising Future Analgesia Directionmentioning

confidence: 99%

From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives

Wang

Duan

et al. 2020

Front. Med.

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Pain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic techniques. A Cochrane systematic review showed that patient-controlled analgesia (PCA) achieved better pain relief and greater patient satisfaction than traditional "on-demand" parenteral analgesia, suggesting that it might be the manner of analgesia implementation that matters for effective postoperative pain management. A wireless intelligent PCA (Wi-PCA) system that incorporated remote monitoring, an intelligent alarm, intelligent analysis and assessment of the PCA equipment, as well as automatically recording and reserving key information functions under a wireless environment was introduced in our department in 2018. The practice showed that the Wi-PCA system significantly reduced the incidence of moderate to severe postoperative pain and relevant adverse effects, shortened hospital stays, and improved patient satisfaction with postoperative pain relief. Nevertheless, for both traditional and Wi-PCA, analgesics are only administered when pain occurs, leaving behind a realm of possibilities for better postoperative pain management. With the rapid development of machinery and deep learning algorithms, artificial intelligence (AI) is changing the mode of clinical decision making. Integrating the big data collected by state-of-the-art monitoring sensors, the Internet of Things and AI algorithms, an AI-assisted PCA (Ai-PCA) may be a promising future direction for postoperative pain management.

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“…Home assistants monitor elderly patients for deteriorating behavioral and emotional conditions. Administratively, AI is being used in voice‐recognition dictation into the EMR 5 . By reducing time spent interacting with the record, physicians are able to spend more time with the patient.…”

Section: What Is “Ai” and How Is It Being Used In Health Care?mentioning

confidence: 99%

“…Smartwatch use has proliferated and there are many health care‐related applications that can track real‐time patient data and predict impending emergency medical conditions 9 . Further, some integrated voice‐activated home computing devices allow for remote monitoring of home‐bound elderly patients for depression, falls, and other emergencies.…”

Section: Health Care Risk Issues Associated With Remote Monitoring Dementioning

confidence: 99%