A review of gynecological cancers studies of concordance with individual clinicians or multidisciplinary tumor boards for an artificial intelligence-based clinical decision-support system.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14070-e14070
Author(s):  
Yull Edwin Arriaga ◽  
Rezzan Hekmat ◽  
Karlis Draulis ◽  
Suwei Wang ◽  
Anita M Preininger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Therapeutic Options ◽  
Gynecological Cancers ◽  
Ovarian Cancers ◽  
Tumor Boards

e14070 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system that offers potential therapeutic options to cancer-treating physicians. We reviewed studies of concordance between therapeutic options offered by WfO and treatment decisions made by individual clinicians (IC) and multidisciplinary tumor boards (MTB) in practice in gynecological cancers. Methods: We searched PubMed and an internal database to identify peer-reviewed WfO concordance studies of gynecological cancers published between 01/01/2015 and 06/30/2019. Concordance was defined as agreement between therapeutic options recommended or offered for consideration by WfO and treatment decisions made by IC or MTB. Mean concordance was calculated as a weighted average based on the number of patients per study. Statistical significance was evaluated by z-test of two proportions. Results: Our search identified 5 retrospective studies with 635 patients with cervical and ovarian cancers in China and Thailand; 4 compared WfO to MTB and 1 to IC. Overall WfO concordance with MTB and IC for both cancers was 77.2% (SD 11.6%). The concordance between MTB and WfO in cervical and ovarian cancers was 80.5% and 86.2%, respectively ( P = .21); IC concordance with WfO in cervical and ovarian cancers was 65.2% and 73.2%, respectively ( P = .18). MTB concordance with WfO for both cancers combined was 81.5%, significantly higher than the 67.9% IC concordance with WfO for both cancers ( P = .01). Conclusions: Studies of cervical and ovarian cancers demonstrated a statistically significantly higher concordance of MTB and WfO than IC and WFO, suggesting a role for WfO in supporting treatment-decision making in gynecological cancers that aligns with decisions made by MTB. Larger prospective studies are needed to evaluate the technical performance, usability, workflow integration, and clinical impact of WfO in gynecological cancers.[Table: see text]

A Targeted Review of Breast Cancer Studies of Concordance for an Internationally-Implemented Artificially Intelligent Clinical Decision-Support System

2020 ◽  
Author(s):  
Yull Edwin Arriaga ◽  
Rezzan Hekmat ◽  
Karlis Draulis ◽  
Suwei Wang ◽  
Anita M Preininger ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Therapeutic Options ◽  
Weighted Mean ◽  
Tumor Boards

Abstract Background: Breast cancer has the highest incidence and is the leading cause of cancer-related mortality among women worldwide. IBM Watson® for Oncology (WfO), an artificial intelligence-based clinical decision-support system, provides therapeutic options for consideration to cancer-treating physicians. We conducted a targeted review of studies evaluating concordance of therapeutic options offered by the system with treatment decisions by practicing clinicians in breast cancer. Methods: PubMed, EMBASE, Cochrane, trial registers, conference abstracts, and an internal publication database were searched to identify studies evaluating the concordance of system-generated therapeutic options with treatment decisions by individual clinicians and multidisciplinary tumor boards for breast cancer patients reported in peer-reviewed abstracts or papers published in English between 01/01/2015 and 11/15/2019.Results: Ten breast cancer concordance studies (4703 patients) that met the inclusion criteria were identified and analyzed; the identified studies were from China, India, and Thailand. The weighted mean concordance for all studies was 67.4% (SD 16.0%, range 55.0% - 98.0%). The weighted mean concordance of the system with multidisciplinary tumor boards was 88.2%, (SD 9.7%, range 76.5% - 98.0%), which was substantially higher than concordance between the system and individual clinicians (61.5% , SD 10.1%, range 55.0% -76.0%).Conclusion: Concordance between system-generated therapeutic options and treatment decisions of multidisciplinary tumor boards or individual clinicians for breast cancer demonstrated overall agreement between the system and decisions of practicing cancer-treating physicians in China, India and Thailand. As multidisciplinary tumor boards may lead to higher quality clinical decision-making compared to those of individual clinicians in practice, the relatively higher concordance of the system with multidisciplinary tumor boards suggests a role for clinical decision support to inform clinicians of evidence-informed treatment options.

An artificial intelligence-based clinical decision support system for large kidney stone treatment

2019 ◽  
Vol 42 (3) ◽  
pp. 771-779 ◽  
Author(s):  
Tayyebe Shabaniyan ◽  
Hossein Parsaei ◽  
Alireza Aminsharifi ◽  
Mohammad Mehdi Movahedi ◽  
Amin Torabi Jahromi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Kidney Stone ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Stone Treatment

scholarly journals A Clinical Feasibility Study of an Artificial Intelligence-Powered Clinical Decision Support System

2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Session Length ◽  
Positive Effects ◽  
Significant Difference

Objective: We examine the feasibility of an Artificial Intelligence (AI)-powered clinical decision support system (CDSS), which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. Methods: Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with major depressive disorder (MDD) as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. Results: Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. Conclusions: Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies.

Artificial Intelligence in Outpatient Care in Times of Covid-19: A Multidisciplinary Comment on the Impact of a Notional Artificial Intelligence Based Clinical Decision Support System on Outpatient Care for Kidney Transplant Recipients (Preprint)

2020 ◽  
Author(s):  
Bilgin Osmanodja ◽  
Matthias Braun ◽  
Aljoscha Burchardt ◽  
Wiebke Duettmann ◽  
Michelle Fiekens ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Kidney Transplant ◽  
Clinical Decision Support ◽  
Support System ◽  
Outpatient Care ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients

UNSTRUCTURED The Covid-19 pandemic has put new demands on the medical systems worldwide. The pressure of taking far-reaching decisions within multiply limited resources under the constraint that personal contact must be minimized has evoked the question if technical support in the form of Artificial Intelligence (AI) could help leverage these challenges. At the same time, AI comes with its own issues such as limited transparency that cannot be neglected especially in a medical context. We will deliberate this in the domain of specialized outpatient care of kidney transplant recipients. In order to improve long-term care for these patients, we implemented a telemedicine functionality monitoring vital signs, medication adherence and symptoms at Charité – Universitätsmedizin Berlin. This paper seeks to combine this established telemonitoring approach with methods from Artificial Intelligence proposing an AI-based clinical decision support system (AI-CDSS) that aims to detect Covid-19 and other severe diseases in this high-risk population. After analyzing medical needs and difficulties and suggesting possible technical solutions, we argue that AI-supported telemonitoring in outpatient care can play a valuable role in managing resources and risks in kidney transplant patients in times of Covid-19 and beyond. Additionally, regarding the multitude of ethical and legal questions arising when integrating AI into workflows, we exemplarily discuss the concept of meaningful human control and whether it is achievable with the proposed AI-CDSS.

Artificial intelligence-based clinical decision-support system improves cancer treatment and patient satisfaction.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18303-e18303
Author(s):  
Zuochao Wang ◽  
Zhonghe Yu ◽  
Xuejing Zhang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Treatment Options ◽  
Clinical Decision ◽  
Diagnostic Model ◽  
Consultation System

e18303 Background: Traditional diagnostic model for cancer heavily relies on physicians and their teams’ knowledge. However, under this diagnostic model, patients’ source of information is quite limited. Cancer patients usually fill with negative emotion. Lack of knowledge to the disease and treatment options further leads to less confidence to their treatment outcome. Methods: In order to improve their faith in getting proper treatment and the hope for surviving the deadly disease, we has introduced an artificial intelligence based clinical decision-support system, the Watson for Oncology (WFO), since May-2018. WFO is developed by IBM, it assesses information from a patient’s medical record, evaluates medical evidence, and displays potential treatment options. Our oncologist can then apply their own expertise to identify the most appropriate treatment options. We have generated a new 7-step consultation system with the help of WFO. That include 1: introduce the WFO to patients, 2: patients express their demands and expectations, 3: the oncologist presents patient’s medical condition, 4: discussion with other members in the consultation team, 5: input patients’ information into WFO system and review treatment options, 6: discuss and finalize treatment options with patients, 7: feedbacks form patients after consultation. 70 patients who were hospitalized from May-2018 to Dec-2018 were divided into two groups, 50 patients volunteered to be assigned to the new 7-step consultation system and 20 patients stayed with the traditional diagnostic method to find them treatment options. All patients were followed up by questionnaire. Results: The results showed that patients in the 7-step consultation group presented significantly higher satisfaction rate towards treatment options, confidence level to their health care workers, and willingness to follow the treatment option when compared to patients in the traditional diagnostic group. Conclusions: The WFO assisted 7-step consultation system not only provides a more efficient way to find treatment options, but also improves patients’ understanding to their disease and possible side effects towards the treatment. Most importantly, patients build stronger confidence with their health care team and are willing to believe they will benefit from the treatment plans.

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