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Purpose: Due to significant growth in the volume of information produced by cancer research, staying abreast of recent developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of literature available to the scientific community. However, large-scale studies comparing the recommendations of AI and a multidisciplinary team board (MTB) in gastric cancer treatment have rarely been performed. Therefore, a retrospective real-world study was conducted to assess the level of concordance between AI and MTB treatment recommendations. Methods: Treatment recommendations of Watson for Oncology (WFO) and an MTB were retrospectively analyzed 322 patients with gastric cancer from January 2015 to December 2018 and the degree of agreement between them was compared. The patients were divided into concordance and non-concordance groups and factors affecting the concordance rate were analyzed. Results: The concordance rate between the AI and MTB was 86.96%. The concordance rates for each stage were 96.93% for stage I, 88.89% for stages II, 90.91% for stage III, and 45.83% for stage IV, respectively. In the multivariate analysis, age (p-value = 0.000), performance status (p-value = 0.003 for performance score 1; p-value = 0.007 for performance score 2; p-value = 0.000 for performance score 3), and stage IV (p-value = 0.017) had a significant effect on concordance between the MTB and WFO. Conclusion: Factors affecting the concordance rate were age, performance status, and stage IV gastric cancer. To increase the validity of future medical AI systems for gastric cancer treatment, their supplementation with local guidelines and the ability to comprehensively understand individual patients is essential. [ABSTRACT FROM AUTHOR]

The Watson for Oncology (WFO) decision system has been rolled out in many cancers. However, the consistency of treatment for breast cancer is still unclear in relatively economically disadvantaged areas. Patients with postoperative adjuvant stage (January 2017 to December 2017) and advanced-stage breast cancer (January 2014 to December 2018) in northwest of China were included in this study. Patient information was imported to make treatment decisions using Watson version 19.20 analysis and subsequently compared with clinician decisions and analyzed for influencing factors. A total of 263 patients with postoperative adjuvant breast cancer and 200 with advanced breast cancer were included in this study. The overall treatment modality for WFO was in 80.2% and 50.5% agreement with clinicians in the adjuvant and advanced-stage population, respectively. In adjuvant treatment after breast cancer surgery, menopausal status (odds ratio (OR) = 2.89, P = 0.012, 95% CI, 1.260–6.630), histological grade (OR = 0.22, P = 0.019, 95% CI, 0.061–0.781) and tumor stage (OR = 0.22, P = 0.042, 95% CI, 0.050–0.943) were independent factors affecting the concordance between the two stages. In the first-line treatment of advanced breast cancer, hormone receptor status was a factor influencing the consistency of treatment (χ2 = 14.728, P < 0.001). There was good agreement between the WFOs and clinicians' treatment decisions in postoperative adjuvant breast cancer, but poor agreement was observed in patients with advanced breast cancer. [ABSTRACT FROM AUTHOR]

Background IBM Watson for Oncology (WFO) provides physicians with evidence‐based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non‐small cell lung (NSCLC) cancer patients between the updated version of WFO and physicians in our department, in order to reflect the differences of cancer treatment between China and the United States. Methods Retrospective data from 165 patients with advanced NSCLC from September 2014 to March 2018 were entered manually into WFO. WFO recommendations were provided in three categories: recommended, for consideration, and not recommended. Concordance was analyzed by comparing the treatment decisions proposed by WFO with the real treatment. Potential influenced factors were also analyzed. Results Overall, the treatment recommendations were concordant in 73.3% (121/165) of cases. When two alternative drugs such as icotinib and nedaplatin were included as “for consideration,” the total consistency could be elevated from 73.3% to 90.3%(149/165). The logistic regression analysis showed that gender (P = 0.096), ECOG (P = 0.0.502), smoking (P = 0.455), and pathology (P = 0.633) had no effect on consistency, but stages (P = 0.019), including stage ≤III (77.8%, 21/27) and stage IV (93.5%, 129/138) had significant effects on consistency. Conclusions In China, most of the treatment recommendations of WFO are consistent with the real world treatment. Factors such as patient preferences, prices, drug approval and medical insurance are also taken into consideration, and they ultimately affect the inconsistency. To be comprehensively and rapidly applied in China, localization needs to be accelerated by WFO.

Hai-Sheng You,1,* Chun-Xia Gao,1,* Hai-Bin Wang,2 Sai-Sai Luo,1 Si-Ying Chen,1 Ya-Lin Dong,1 Jun Lyu,3 Tao Tian4 1Department of Pharmacy, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2Hangzhou Cognitive N&T. Co., Ltd, Hangzhou, Zhengjiang, People’s Republic of China; 3Clinical Research Center, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 4Department of Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tao TianDepartment of Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, Shaanxi 710061, People’s Republic of ChinaTel +86-13572206784Fax +86-29-85324086Email tiantao0607@163.comJun LyuClinical Research Center, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, Shaanxi 710061, People’s Republic of ChinaTel +86 29 8532 3614Fax +86 29 8532 3473Email lujun2006@xjtu.edu.cnObjective: The disease complexity of metastatic non-small-cell lung cancer (mNSCLC) makes it difficult for physicians to make clinical decisions efficiently and accurately. The Watson for Oncology (WFO) system of artificial intelligence might help physicians by providing fast and precise treatment regimens. This study measured the concordance of the medical treatment regimens of the WFO system and actual clinical regimens, with the aim of determining the suitability of WFO recommendations for Chinese patients with mNSCLC.Methods: Retrospective data of mNSCLC patients were input to the WFO, which generated a treatment regimen (WFO regimen). The actual regimen was made by physicians in a medical team for patients (medical-team regimen). The factors influencing the consistency of the two treatment options were analyzed by univariate and multivariate analyses.Results: The concordance rate was 85.16% between the WFO and medical-team regimens for mNSCLC patients. Logistic regression showed that the concordance differed significantly for various pathological types and gene mutations in two treatment regimens. Patients with adenocarcinoma had a lower rate of “recommended” regimen than those with squamous cell carcinoma. There was a statistically significant difference in EGFR-mutant patients for “not recommended” regimens with inconsistency rate of 18.75%. In conclusion, the WFO regimen has 85.16% consistency rate with medical-team regimen in our treatment center. The different pathological type and different gene mutation markedly influenced the agreement rate of the two treatment regimens.Conclusion: WFO recommendations have high applicability to mNSCLC patients in our hospital. This study demonstrates that the valuable WFO system may assist the doctors better to determine the accurate and effective treatment regimens for mNSCLC patients in the Chinese medical setting.Keywords: metastatic non-small-cell lung cancer, Watson for Oncology, concordance, artificial intelligence, treatment recommendations

Purpose Watson for Oncology (WFO) is a computing system for considering treatment option with patients. The aim of this study is to determine the concordance rate of WFO and tumor board in treatment options of breast cancer. Methods One hundred and seventy breast cancer patients who were treated at Gachon University Gil Medical Center (GMC) from December 2016 to March 2018 were investigated. “Concordance” is defined that treatment provided by GMC tumor board and WFO-provided treatments (Recommend or For Consideration) were coincide. “Discordance” is defined that treatment provided by GMC tumor board and WFO-provided treatments (Not Recommended or Not Available) were coincide. Results In chemotherapy, of the total 170 patients, 23 patients were excluded, and the concordance rate was compared in 147 patients. Concordance rate was 93% (136/147). In the 11 patients who were discordant, seven patients complied with the treatment proposed by GMC tumor board due to insurance problems and age, and two patients did not follow the treatment proposed by WFO due to pregnancy and patient’s choice, and one patient chose a treatment determined by WFO, last one patient was not treated with chemotherapy because it was not considered to be clinically necessary. In radiotherapy, the overall agreement rate was as high as 99% (147/148). Conclusion Through this study, we found that many of the treatment proposals provided by WFO are highly reliable. Although the overall agreement is high in the radiotherapy regimen, the indication for radiotherapy by WFO is more aggressive and hypofractionated high dose intensity radiotherapy is growing trend by WFO.

BackgroundIBM’s Watson for Oncology (WFO) is an artificial intelligence tool that trains by acquiring data from the Memorial Sloan Kettering Cancer Center and learns from test cases and experts. This study aimed to analyze the adequacy and effectiveness of WFO in determining the treatment method for patients with thyroid carcinoma.Materials and MethodsWe retrospectively enrolled 50 patients with thyroid cancer who underwent surgery in 2018 and entered their clinical data into WFO. The WFO treatment recommendations were compared with the surgical procedures and recommended treatments performed according to the Korean Thyroid Endocrine Surgery Association guidelines.ResultsThe overall concordance rate between WFO-recommended treatments and actual surgical treatments was 48%, and for patients with stage I, II, and III disease, these rates were 52.4, 50, and 16.7%, respectively. A lower concordance rate was observed with respect to treatment for advanced thyroid cancer.ConclusionWFO is a useful clinical aid but must be used with caution. A surgeon’s decision takes precedence over WFO recommendations in the treatment of advanced thyroid cancer.

Objective: IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice.Methods: This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH's institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage.Results: Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest.Conclusion: This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US. [ABSTRACT FROM AUTHOR]

Background: IBM's Watson for Oncology (WFO) is an artificial intelligence tool that trains by acquiring data from the Memorial Sloan Kettering Cancer Center and learns from test cases and experts. This study aimed to analyze the adequacy and effectiveness of WFO in determining the treatment method for patients with thyroid carcinoma. Materials and Methods: We retrospectively enrolled 50 patients with thyroid cancer who underwent surgery in 2018 and entered their clinical data into WFO. The WFO treatment recommendations were compared with the surgical procedures and recommended treatments performed according to the Korean Thyroid Endocrine Surgery Association guidelines. Results: The overall concordance rate between WFO-recommended treatments and actual surgical treatments was 48%, and for patients with stage I, II, and III disease, these rates were 52.4, 50, and 16.7%, respectively. A lower concordance rate was observed with respect to treatment for advanced thyroid cancer. Conclusion: WFO is a useful clinical aid but must be used with caution. A surgeon's decision takes precedence over WFO recommendations in the treatment of advanced thyroid cancer. [ABSTRACT FROM AUTHOR]

BackgroundPersonalized and novel evidence-based clinical treatment strategy consulting for colorectal cancer has been available through various artificial intelligence (AI) supporting systems such as Watson for Oncology (WFO) from IBM. However, the potential effects of this supporting tool in cancer care have not been thoroughly explored in real-world studies. This research aims to investigate the concordance between treatment recommendations for colorectal cancer patients made by WFO and a multidisciplinary team (MDT) at a major comprehensive gastrointestinal cancer center.MethodsIn this prospective study, both WFO and the blinded MDT’s treatment recommendations were provided concurrently for enrolled colorectal cancers of stages II to IV between March 2017 and January 2018 at Shanghai Minimally Invasive Surgery Center. Concordance was achieved if the cancer team’s decisions were listed in the “recommended” or “for consideration” classification in WFO. A review was carried out after 100 cases for all non-concordant patients to explain the inconsistency, and corresponding feedback was given to WFO’s database. The concordance of the subsequent cases was analyzed to evaluate both the performance and learning ability of WFO.ResultsOverall, 250 patients met the inclusion criteria and were recruited in the study. Eighty-one were diagnosed with colon cancer and 189 with rectal cancer. The concordances for colon cancer, rectal cancer, or overall were all 91%. The overall rates were 83, 94, and 88% in subgroups of stages II, III, and IV. When categorized by treatment strategy, concordances were 97, 93, 89, 87, and 100% for neoadjuvant, surgery, adjuvant, first line, and second line treatment groups, respectively. After analyzing the main factors causing discordance, relative updates were made in the database accordingly, which led to the concordance curve rising in most groups compared with the initial rates.ConclusionClinical recommendations made by WFO and the cancer team were highly matched for colorectal cancer. Patient age, cancer stage, and the consideration of previous therapy details had a significant influence on concordance. Addressing these perspectives will facilitate the use of the cancer decision-support systems to help oncologists achieve the promise of precision medicine.

BackgroundWatson for Oncology (WFO) is a cognitive computing system that provides clinical decision support. This study examined the concordance between the treatment recommendations for colorectal cancer (CRC) proposed by WFO and those recommended by the multidisciplinary teams (MDTs), and evaluated the influence of concordance on the prognosis.MethodsWe retrospectively collected 175 patients with colorectal cancer who received treatment recommended by MDTs at a hospital in China, and evaluated them using WFO. Concordance between the two recommendations was analyzed. The overall survival was analyzed between concordant and non-concordant groups. Logistic regression analyses were performed and a concordance-predicting model was developed.ResultsConcordance between WFO’ and MDTs’ recommendations occurred in 66.9% (117/175) of cases. The overall survival (OS) was significantly better in concordant group and non-concordance was found to be an independent prognostic factor [hazard ratio (HR)=2.784 (95% CI 1.264–6.315)]. Logistic regression analyses determined that tumor type [odds ratio (OR)= 2.195 for left colon cancer and OR=2.502 for rectum cancer], and TNM stage (OR=0.545 for stage II, OR=0.187 for stage III, OR=0.127 for stage IV) were independently related with concordance, which were used to develop a concordance-predictive-nomogram.ConclusionsTreatment recommendations for patients with colorectal cancer determined by WFO and MDTs were mostly concordant. However, the survival was better among concordant patients and non-concordance was found to be an independent prognostic factor. This study presents a nomogram that can be conveniently used for predicting individualized concordance. However, our findings should be prospectively validated in multi-center trials.

Watson for Oncology (WFO) is a artificial intelligence clinical decision-support system with evidence-based treatment options for oncologists. WFO has been gradually used in China, but limited reports on whether WFO is suitable for Chinese patients. This study aims to investigate the concordance of treatment options between WFO and real clinical practice for Cervical cancer patients retrospectively. We retrospectively enrolled 300 cases of cervical cancer patients. WFO provides treatment options for 246 supported cases. Real clinical practice were defined as concordant if treatment options were designated “recommended” or “for consideration” by WFO. Concordance of treatment option between WFO and real clinical practice was analyzed statistically. The treatment concordance between WFO and real clinical practice occurred in 72.8% (179/246) of cervical cancer cases. Logistic regression analysis showed that rural registration residences, advanced age, poor ECOG performance status, stages II-IV disease have a remarkable impact on consistency. The main reasons attributed to the 27.2% (67/246) of the discordant cases were the substitution of nedaplatin for cisplatin, reimbursement plan of bevacizumab, surgical preference, and absence of neoadjuvant/adjuvant chemotherapy and PD-1/PD-L1 antibodies recommendations. WFO recommendations were in 72.8% of concordant with real clinical practice for cervical cancer patients in China. However, several localization and individual factors limit its wider application. So, WFO could be an essential tool but it cannot currently replace oncologists. To be rapidly and fully apply to cervical cancer patients in China, accelerate localization and improvement were needed for WFO.

Background: Personalized and novel evidence-based clinical treatment strategy consulting for colorectal cancer has been available through various artificial intelligence (AI) supporting systems such as Watson for Oncology (WFO) from IBM. However, the potential effects of this supporting tool in cancer care have not been thoroughly explored in real-world studies. This research aims to investigate the concordance between treatment recommendations for colorectal cancer patients made by WFO and a multidisciplinary team (MDT) at a major comprehensive gastrointestinal cancer center. Methods: In this prospective study, both WFO and the blinded MDT's treatment recommendations were provided concurrently for enrolled colorectal cancers of stages II to IV between March 2017 and January 2018 at Shanghai Minimally Invasive Surgery Center. Concordance was achieved if the cancer team's decisions were listed in the "recommended" or "for consideration" classification in WFO. A review was carried out after 100 cases for all non-concordant patients to explain the inconsistency, and corresponding feedback was given to WFO's database. The concordance of the subsequent cases was analyzed to evaluate both the performance and learning ability of WFO. Results: Overall, 250 patients met the inclusion criteria and were recruited in the study. Eighty-one were diagnosed with colon cancer and 189 with rectal cancer. The concordances for colon cancer, rectal cancer, or overall were all 91%. The overall rates were 83, 94, and 88% in subgroups of stages II, III, and IV. When categorized by treatment strategy, concordances were 97, 93, 89, 87, and 100% for neoadjuvant, surgery, adjuvant, first line, and second line treatment groups, respectively. After analyzing the main factors causing discordance, relative updates were made in the database accordingly, which led to the concordance curve rising in most groups compared with the initial rates. Conclusion: Clinical recommendations made by WFO and the cancer team were highly matched for colorectal cancer. Patient age, cancer stage, and the consideration of previous therapy details had a significant influence on concordance. Addressing these perspectives will facilitate the use of the cancer decision-support systems to help oncologists achieve the promise of precision medicine. [ABSTRACT FROM AUTHOR]

Purpose: Urological oncologists have difficulty providing optimal personalized care due to rapid alterations in scientific research results, medical advancements, and treatment guidelines. IBM's Watson for Oncology (WFO) is an artificial intelligence clinical decision-support system that assists oncologists with evidence-based treatment recommendations. In the present study, we examined the level of concordance between the treatment recommendations for prostate cancer according to WFO and the actual treatments that the patients received in the department of urology. Methods: We enrolled 201 patients who received prostate cancer treatment between January 2018 and June 2018. WFO provided treatment recommendations using clinical data in three categories: recommended, for consideration, and not recommended. These were compared with the actual treatments received by patients. Prostate cancer treatments were considered concordant if the received treatments were included in the "recommended" or "for consideration" categories by WFO. Results: The patients' mean age was 71.2 years. There were 60 (29.9%) and 114 (56.7%) patients with an Eastern Cooperative Oncology Group (ECOG) performance score ≥ 1 and non-organ confined disease (stage III/IV), respectively. The overall prostate cancer treatment concordance rate was 73.6% ("recommended": 53.2%; "for consideration": 20.4%). An ECOG performance score ≥ 1 and older age (≥ 75 years) were significantly associated with discordance (p = 0.001 and p = 0.026, respectively) on multivariate analysis. Conclusion: In the present study, the treatment recommendations by WFO and the actual received treatments in the department of urology showed a relatively high concordance rate in prostate cancer patients. [ABSTRACT FROM AUTHOR]

Background: Watson for Oncology (WFO) is a cognitive computing system that provides clinical decision support. This study examined the concordance between the treatment recommendations for colorectal cancer (CRC) proposed by WFO and those recommended by the multidisciplinary teams (MDTs), and evaluated the influence of concordance on the prognosis. Methods: We retrospectively collected 175 patients with colorectal cancer who received treatment recommended by MDTs at a hospital in China, and evaluated them using WFO. Concordance between the two recommendations was analyzed. The overall survival was analyzed between concordant and non-concordant groups. Logistic regression analyses were performed and a concordance-predicting model was developed. Results: Concordance between WFO' and MDTs' recommendations occurred in 66.9% (117/175) of cases. The overall survival (OS) was significantly better in concordant group and non-concordance was found to be an independent prognostic factor [hazard ratio (HR)=2.784 (95% CI 1.264–6.315)]. Logistic regression analyses determined that tumor type [odds ratio (OR)= 2.195 for left colon cancer and OR=2.502 for rectum cancer], and TNM stage (OR=0.545 for stage II, OR=0.187 for stage III, OR=0.127 for stage IV) were independently related with concordance, which were used to develop a concordance-predictive-nomogram. Conclusions: Treatment recommendations for patients with colorectal cancer determined by WFO and MDTs were mostly concordant. However, the survival was better among concordant patients and non-concordance was found to be an independent prognostic factor. This study presents a nomogram that can be conveniently used for predicting individualized concordance. However, our findings should be prospectively validated in multi-center trials. [ABSTRACT FROM AUTHOR]

Machine learning platforms have emerged as a new promissory technology that some argue will revolutionize work practices across a broad range of professions, including medical care. During the past few years, IBM has been testing its Watson for Oncology platform at several oncology departments around the world. Published reports, news stories, as well as our own empirical research show that in some cases, the levels of concordance over recommended treatment protocols between the platform and human oncologists have been quite low. Other studies supported by IBM claim concordance rates as high as 96%. We use the Watson for Oncology case to examine the practice of using concordance levels between tumor boards and a machine learning decision-support system as a form of evidence. We address a challenge related to the epistemic authority between oncologists on tumor boards and the Watson Oncology platform by arguing that the use of concordance levels as a form of evidence of quality or trustworthiness is problematic. Although the platform provides links to the literature from which it draws its conclusion, it obfuscates the scoring criteria that it uses to value some studies over others. In other words, the platform "black boxes" the values that are coded into its scoring system. [ABSTRACT FROM AUTHOR]

Background: IBM Watson for Oncology (WFO) provides physicians with evidence‐based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non‐small cell lung (NSCLC) cancer patients between the updated version of WFO and physicians in our department, in order to reflect the differences of cancer treatment between China and the United States. Methods: Retrospective data from 165 patients with advanced NSCLC from September 2014 to March 2018 were entered manually into WFO. WFO recommendations were provided in three categories: recommended, for consideration, and not recommended. Concordance was analyzed by comparing the treatment decisions proposed by WFO with the real treatment. Potential influenced factors were also analyzed. Results: Overall, the treatment recommendations were concordant in 73.3% (121/165) of cases. When two alternative drugs such as icotinib and nedaplatin were included as "for consideration," the total consistency could be elevated from 73.3% to 90.3%(149/165). The logistic regression analysis showed that gender (P = 0.096), ECOG (P = 0.0.502), smoking (P = 0.455), and pathology (P = 0.633) had no effect on consistency, but stages (P = 0.019), including stage ≤III (77.8%, 21/27) and stage IV (93.5%, 129/138) had significant effects on consistency. Conclusions: In China, most of the treatment recommendations of WFO are consistent with the real world treatment. Factors such as patient preferences, prices, drug approval and medical insurance are also taken into consideration, and they ultimately affect the inconsistency. To be comprehensively and rapidly applied in China, localization needs to be accelerated by WFO. [ABSTRACT FROM AUTHOR]

Background: To the authors' knowledge, the indications for radioactive iodine (RAI) therapy in patients with differentiated thyroid carcinoma (DTC) are unclear; treatment decisions are based on physician judgment. The objective of the current study was to identify the degree of concordance between postsurgical RAI therapy recommended by Watson for Oncology (WFO), a clinical decision support system for oncological therapy, and that recommended by physicians for patients with DTC.Methods: The current retrospective cohort study included 207 patients with DTC who underwent thyroidectomy between 2017 and 2018. Treatment recommendations were considered concordant if WFO rendered recommendations consistent with those of the physicians.Results: Treatment recommendations were concordant for 160 patients (77%). The concordance rate significantly differed according to the American Thyroid Association (ATA) risk category (P < .001) and American Joint Committee on Cancer TNM stage (seventh edition; P = .004). Logistic regression analysis demonstrated that treatment recommendations were significantly less likely to be concordant in patients with ATA intermediate-risk and stage III disease compared with those with ATA low-risk and stage I disease (odds ratio, 0.16 [P < .001] and OR, 0.35 [P = .004], respectively).Conclusions: The authors believe the concordance rate between postsurgical RAI therapy recommendations rendered by WFO and those rendered by physicians was too low to justify adopting WFO for the comprehensive screening of patients with DTC. This is particularly true among patients with ATA intermediate-risk and stage III disease, reflecting differences in practice patterns between the United States (where WFO was calibrated) and Korea. Hence, WFO is not a substitute for physicians, and also may require regional customization to improve its assistive capability. [ABSTRACT FROM AUTHOR]

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Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (MDTs) caring for cancer patients. This study aimed to investigate the effect of WfO use on hospital satisfaction and perception among patients cared for by MDTs. This was a descriptive study that used a written survey to gather information from cancer patients at a hospital in Korea. The rate of positive change in patient perception after treatment was 86.8% in the MDT-WfO group and 71.2% in the MDT group. In terms of easily understandable explanations, the MDT-WfO (9.53 points) group reported higher satisfaction than the MDT group (9.24 points). Younger patients in the MDT-WfO group showed high levels of satisfaction and reliability of treatment. When WfO was used, the probability of positive change in patient perception of the hospital was 2.53 times higher than when WfO was not used. With a one-point increase in overall satisfaction, the probability of positive change in patient perception of the hospital increased 1.97 times. Therefore, if WfO is used appropriately in the medical field, it may enhance patient satisfaction and change patient perception positively.

Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer. Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in 2016. A blinded second review was carried out by the center's tumor board in 2016 for all cases in which there was not agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered concordant if the tumor board recommendations were designated 'recommended' or 'for consideration' by WFO. Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases. Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P≤0.02; P<0.001) in all age groups compared with patients<45 years of age, except for the age group 55-64 years. Receptor status was not found to affect concordance. Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not. This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment decision making, especially at centers where expert breast cancer resources are limited. [ABSTRACT FROM AUTHOR]

As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians' perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle-income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle-income countries for cancer care., (©Srinivas Emani, Angela Rui, Hermano Alexandre Lima Rocha, Rubina F Rizvi, Sergio Ferreira Juaçaba, Gretchen Purcell Jackson, David W Bates. Originally published in JMIR Cancer (https://cancer.jmir.org), 07.04.2022.)

Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (MDTs) caring for cancer patients. This study aimed to investigate the effect of WfO use on hospital satisfaction and perception among patients cared for by MDTs. This was a descriptive study that used a written survey to gather information from cancer patients at a hospital in Korea. The rate of positive change in patient perception after treatment was 86.8% in the MDT-WfO group and 71.2% in the MDT group. In terms of easily understandable explanations, the MDT-WfO (9.53 points) group reported higher satisfaction than the MDT group (9.24 points). Younger patients in the MDT-WfO group showed high levels of satisfaction and reliability of treatment. When WfO was used, the probability of positive change in patient perception of the hospital was 2.53 times higher than when WfO was not used. With a one-point increase in overall satisfaction, the probability of positive change in patient perception of the hospital increased 1.97 times. Therefore, if WfO is used appropriately in the medical field, it may enhance patient satisfaction and change patient perception positively. [ABSTRACT FROM AUTHOR]

Objective: Watson for Oncology (WFO), an artificial intelligence from IBM Corporation, can provide a treatment plan by analyzing patient's disease characteristics. The present study was performed to examine the concordance between treatment recommendations proposed by WFO and the multidisciplinary tumor board at our center. The aim was to explore the feasibility of using WFO for breast cancer cases in China and to ascertain the ways to make WFO more suitable for Chinese patients with breast cancer., Methods: Data from 302 breast cancer patients treated at the Second Affiliated Hospital of Xi'an Jiaotong University between October 2016 and February 2018 was retrieved and retrospectively analyzed by WFO. The recommendations were divided into 'recommended', 'considered' and 'not recommended' groups. Results were considered concordant when oncologists' recommendations were categorized as 'recommended' or 'for consideration' by WFO., Results: The concordance rate of 200 subjects with postoperative adjuvant therapy was 77%. However, the rate was 27.5% in the remaining 102 cases with metastatic disease receiving either first-line or no treatment. Further analysis demonstrated that inconsistencies were mainly due to different choices of chemotherapy regimens. Subgroup study indicates that tumor stage, receptor status and age also had influences at the concordance rate., Conclusion: The results of this study suggest that WFO is a promising artificial intelligence system for the treatment of breast cancer. These findings can also serve as a reference framework for the inclusion of artificial intelligence in the ongoing medical reform in China., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: With the increasing number of cancer treatments, the emergence of multidisciplinary teams (MDTs) provides patients with personalized treatment options. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. There has been a gradual tendency to replace traditional diagnosis and treatment with AI. IBM Watson for Oncology (WFO) has been proven to be useful for decision-making in breast cancer and lung cancer, but to date, research on gastric cancer is limited.Objective: This study compared the concordance of WFO with MDT and investigated the impact on patient prognosis.Methods: This study retrospectively analyzed eligible patients (N=235) with gastric cancer who were evaluated by an MDT, received corresponding recommended treatment, and underwent follow-up. Thereafter, physicians inputted the information of all patients into WFO manually, and the results were compared with the treatment programs recommended by the MDT. If the MDT treatment program was classified as "recommended" or "considered" by WFO, we considered the results concordant. All patients were divided into a concordant group and a nonconcordant group according to whether the WFO and MDT treatment programs were concordant. The prognoses of the two groups were analyzed.Results: The overall concordance of WFO and the MDT was 54.5% (128/235) in this study. The subgroup analysis found that concordance was less likely in patients with human epidermal growth factor receptor 2 (HER2)-positive tumors than in patients with HER2-negative tumors (P=.02). Age, Eastern Cooperative Oncology Group performance status, differentiation type, and clinical stage were not found to affect concordance. Among all patients, the survival time was significantly better in concordant patients than in nonconcordant patients (P<.001). Multivariate analysis revealed that concordance was an independent prognostic factor of overall survival in patients with gastric cancer (hazard ratio 0.312 [95% CI 0.187-0.521]).Conclusions: The treatment recommendations made by WFO and the MDT were mostly concordant in gastric cancer patients. If the WFO options are updated to include local treatment programs, the concordance will greatly improve. The HER2 status of patients with gastric cancer had a strong effect on the likelihood of concordance. Generally, survival was better in concordant patients than in nonconcordant patients. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is developing quickly in the medical field and can benefit both medical staff and patients. The clinical decision support system Watson for Oncology (WFO) is an outstanding representative AI in the medical field, and it can provide to cancer patients prompt treatment recommendations comparable with ones made by expert oncologists. WFO is increasingly being used in China, but limited reports on whether WFO is suitable for Chinese patients, especially patients with lung cancer, exist. Here, we report a retrospective study based on the consistency between the lung cancer treatment recommendations made for the same patient by WFO and by the multidisciplinary team at our center.Objective: The aim of this study was to explore the feasibility of using WFO for lung cancer cases in China and to ascertain ways to make WFO more suitable for Chinese patients with lung cancer.Methods: We selected all lung cancer patients who were hospitalized and received antitumor treatment for the first time at the Second Xiangya Hospital Cancer Center from September to December 2017 (N=182). WFO made treatment recommendations for all supported cases (n=149). If the actual therapeutic regimen (administered by our multidisciplinary team) was recommended or for consideration according to WFO, we defined the recommendations as consistent; if the actual therapeutic regimen was not recommended by WFO or if WFO did not provide the same treatment option, we defined the recommendations as inconsistent. Blinded second round reviews were performed by our multidisciplinary team to reassess the incongruent cases.Results: WFO did not support 18.1% (33/182) of recommendations among all cases. Of the 149 supported cases, 65.8% (98/149) received recommendations that were consistent with the recommendations of our team. Logistic regression analysis showed that pathological type and staging had significant effects on consistency (P=.004, odds ratio [OR] 0.09, 95% CI 0.02-0.45 and P<.001, OR 9.5, 95% CI 3.4-26.1, respectively). Age, gender, and presence of epidermal growth factor receptor gene mutations had no effect on consistency. In 82% (42/51) of the inconsistent cases, our team administered two China-specific treatments, which were different from the recommendations made by WFO but led to excellent outcomes.Conclusions: In China, most of the treatment recommendations of WFO are consistent with the recommendations of the expert group, although a relatively high proportion of cases are still not supported by WFO. Therefore, WFO cannot currently replace oncologists. WFO can improve the efficiency of clinical work by providing assistance to doctors, but it needs to learn the regional characteristics of patients to improve its assistive ability. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is developing quickly in the medical field and can benefit both medical staff and patients. The clinical decision support system Watson for Oncology (WFO) is an outstanding representative AI in the medical field, and it can provide to cancer patients prompt treatment recommendations comparable with ones made by expert oncologists. WFO is increasingly being used in China, but limited reports on whether WFO is suitable for Chinese patients, especially patients with lung cancer, exist. Here, we report a retrospective study based on the consistency between the lung cancer treatment recommendations made for the same patient by WFO and by the multidisciplinary team at our center., Objective: The aim of this study was to explore the feasibility of using WFO for lung cancer cases in China and to ascertain ways to make WFO more suitable for Chinese patients with lung cancer., Methods: We selected all lung cancer patients who were hospitalized and received antitumor treatment for the first time at the Second Xiangya Hospital Cancer Center from September to December 2017 (N=182). WFO made treatment recommendations for all supported cases (n=149). If the actual therapeutic regimen (administered by our multidisciplinary team) was recommended or for consideration according to WFO, we defined the recommendations as consistent; if the actual therapeutic regimen was not recommended by WFO or if WFO did not provide the same treatment option, we defined the recommendations as inconsistent. Blinded second round reviews were performed by our multidisciplinary team to reassess the incongruent cases., Results: WFO did not support 18.1% (33/182) of recommendations among all cases. Of the 149 supported cases, 65.8% (98/149) received recommendations that were consistent with the recommendations of our team. Logistic regression analysis showed that pathological type and staging had significant effects on consistency (P=.004, odds ratio [OR] 0.09, 95% CI 0.02-0.45 and P<.001, OR 9.5, 95% CI 3.4-26.1, respectively). Age, gender, and presence of epidermal growth factor receptor gene mutations had no effect on consistency. In 82% (42/51) of the inconsistent cases, our team administered two China-specific treatments, which were different from the recommendations made by WFO but led to excellent outcomes., Conclusions: In China, most of the treatment recommendations of WFO are consistent with the recommendations of the expert group, although a relatively high proportion of cases are still not supported by WFO. Therefore, WFO cannot currently replace oncologists. WFO can improve the efficiency of clinical work by providing assistance to doctors, but it needs to learn the regional characteristics of patients to improve its assistive ability., (©Chaoyuan Liu, Xianling Liu, Fang Wu, Mingxuan Xie, Yeqian Feng, Chunhong Hu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.09.2018.)

Background and Aim: Medical specialists aim to provide evidence-based care based on the most recent scientific insights, but with the ongoing expansion of medical literature it seems unfeasible to remain updated. "Black-box" decision support tools such as Watson for Oncology (Watson) are gaining attention as they offer a promising opportunity to conquer this challenging issue, but it is not known if the advice given is congruent with guidelines or clinically valid in other settings. We present a protocol for the content evaluation of black-box decision support tools and a feasibility study to test the content and usability of Watson using this protocol., Methods: The protocol consists of developing synthetic patient cases based on Dutch guidelines and expert opinion, entering the synthetic cases into Watson and Oncoguide, noting the response of each system and evaluating the result using a cross-tabulation scoring system resulting in a score range of -12 to +12. Treatment options that were not recommended according to the Dutch guideline were labeled with a "red flag" if Watson recommended it, and an "orange flag" if Watson suggested it for consideration. To test the feasibility of applying the protocol, we developed synthetic patient cases for the adjuvant treatment of stage I to stage III colon cancer based on relevant patient, clinical and tumor characteristics and followed our protocol. Additionally, for the feasibility study we also compared the recommendations from the NCCN guideline with Watson's advice, and evaluated usability by a cognitive walkthrough method., Results: In total, we developed 190 synthetic patient cases (stage I: n=8; stage II: n=110; and stage III: n=72). Overall concordance scores per case for Watson versus Oncoguide ranged from a minimum score of -4 (n=6) to a maximum score of+12 (n=17) and from -4 (n=9) to +12 (n=24) for Watson versus the NCCN guidelines). In total, 69 cases (36%) were labeled with red flags, 96 cases (51%) with orange flags and 25 cases (13%) without flags. For the comparison of Watson with the NCCN guidelines, no red or orange flags were identified., Conclusions: We developed a research protocol for the evaluation of a black-box decision support tool, which proved useful and usable in testing the content and usability of Watson. Overall concordance scores ranged considerably between synthetic cases for both comparisons between Watson versus Oncoguide and Watson versus NCCN. Non-concordance is partially attributable to guideline differences between the United States and The Netherlands. This implies that further adjustments and localization are required before implementation of Watson outside the United States., Relevance for Patients: This study describes the first steps of content evaluation of a decision support tool before implementation in daily oncological patient care. The ultimate goal of the incorporation of decision support tools in daily practice is to improve personalized medicine and quality of care.