Published on in Vol 20, No 9 (2018): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11087, first published .
Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study

Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study

Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study

Journals

  1. Lu Y. Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics 2019;6(1):1 View
  2. Tupasela A, Di Nucci E. Concordance as evidence in the Watson for Oncology decision-support system. AI & SOCIETY 2020;35(4):811 View
  3. Zou F, Tang Y, Liu C, Ma J, Hu C. Concordance Study Between IBM Watson for Oncology and Real Clinical Practice for Cervical Cancer Patients in China: A Retrospective Analysis. Frontiers in Genetics 2020;11 View
  4. Kim M, Kim B, Kim J, Kim E, Kim K, Pak K, Jeon Y, Kim S, Park H, Kang T, Lee B, Kim I. Concordance in postsurgical radioactive iodine therapy recommendations between Watson for Oncology and clinical practice in patients with differentiated thyroid carcinoma. Cancer 2019;125(16):2803 View
  5. Fiske A, Henningsen P, Buyx A. Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy. Journal of Medical Internet Research 2019;21(5):e13216 View
  6. Morley J, Machado C, Burr C, Cowls J, Taddeo M, Floridi L. The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review. SSRN Electronic Journal 2019 View
  7. Fischer I, Steiger H. Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke. Journal of Stroke and Cerebrovascular Diseases 2020;29(9):105042 View
  8. Zhang W, Qi S, Zhuo J, Wen S, Fang C. Concordance Study in Hepatectomy Recommendations Between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China. World Journal of Surgery 2020;44(6):1945 View
  9. Tian Y, Liu X, Wang Z, Cao S, Liu Z, Ji Q, Li Z, Sun Y, Zhou X, Wang D, Zhou Y. Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study. Journal of Medical Internet Research 2020;22(2):e14122 View
  10. Herrmann D, Oggiano M, Hecker E. Einsatz künstlicher Intelligenz in der Thoraxchirurgie. Der Chirurg 2020;91(3):206 View
  11. Shekarriz J, Keck T, Shekarriz H. Computerized Medical Evidence-Based Decision Assistance System “MEBDAS®" improves in-hospital outcome after pancreatoduodenectomy for pancreatic cancer. Pancreatology 2020;20(4):746 View
  12. Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020;2020 View
  13. Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomedicine & Pharmacotherapy 2020;128:110255 View
  14. You H, Gao C, Wang H, Luo S, Chen S, Dong Y, Lyu J, Tian T. <p>Concordance of Treatment Recommendations for Metastatic Non-Small-Cell Lung Cancer Between Watson for Oncology System and Medical Team</p>. Cancer Management and Research 2020;Volume 12:1947 View
  15. Morley J, Floridi L. An ethically mindful approach to AI for health care. The Lancet 2020;395(10220):254 View
  16. Yao S, Wang R, Qian K, Zhang Y. Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non‐small cell lung cancer patients at a lung cancer center in China. Thoracic Cancer 2020;11(5):1265 View
  17. Zhao X, Zhang Y, Ma X, Chen Y, Xi J, Yin X, Kang H, Guan H, Dai Z, Liu D, Zhao F, Sun C, Li Z, Zhang S. Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China. Japanese Journal of Clinical Oncology 2020;50(8):852 View
  18. Maurer L, El Hechi M, Kaafarani H. When Artificial Intelligence Disagrees With the Doctor, Who’s Right? The Answer Might Not Be So Evident. Diseases of the Colon & Rectum 2020;63(10):1347 View
  19. Zhou J, Zeng Z, Li L. <p>Progress of Artificial Intelligence in Gynecological Malignant Tumors</p>. Cancer Management and Research 2020;Volume 12:12823 View
  20. Aikemu B, Xue P, Hong H, Jia H, Wang C, Li S, Huang L, Ding X, Zhang H, Cai G, Lu A, Xie L, Li H, Zheng M, Sun J. Artificial Intelligence in Decision-Making for Colorectal Cancer Treatment Strategy: An Observational Study of Implementing Watson for Oncology in a 250-Case Cohort. Frontiers in Oncology 2021;10 View
  21. Yin J, Ngiam K, Teo H. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of Medical Internet Research 2021;23(4):e25759 View
  22. Jie Z, Zhiying Z, Li L. A meta-analysis of Watson for Oncology in clinical application. Scientific Reports 2021;11(1) View
  23. Kiryu Y. Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy. YAKUGAKU ZASSHI 2021;141(2):179 View
  24. Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagnostic Pathology 2021;16(1) View
  25. Emani S, Rui A, Rocha H, Rizvi R, Juaçaba S, Jackson G, Bates D. Physicians’ Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle–Income Countries. JMIR Cancer 2022;8(2):e31461 View
  26. Ameen S, Wong M, Yee K, Turner P. AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening. Applied Sciences 2022;12(7):3341 View
  27. Holohan M, Fiske A. “Like I’m Talking to a Real Person”: Exploring the Meaning of Transference for the Use and Design of AI-Based Applications in Psychotherapy. Frontiers in Psychology 2021;12 View
  28. Yung A, Kay J, Beale P, Gibson K, Shaw T. Computer-Based Decision Tools for Shared Therapeutic Decision-making in Oncology: Systematic Review. JMIR Cancer 2021;7(4):e31616 View
  29. Liu F, Jiang X, Zhang C, Wang G, Li Y, Pang B, Emran T. The Origin and Development of Piji Pills: An Ancient Prescription of Traditional Chinese Medicine. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  30. Zhang K, Chen K. Artificial intelligence: opportunities in lung cancer. Current Opinion in Oncology 2022;34(1):44 View
  31. Huang P, Kim K, Schermer M. Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study. Journal of Medical Internet Research 2022;24(1):e33081 View
  32. Talwar V, Chufal K, Joga S. Artificial Intelligence: A New Tool in Oncologist's Armamentarium. Indian Journal of Medical and Paediatric Oncology 2021;42(06):511 View
  33. Morley J, Murphy L, Mishra A, Joshi I, Karpathakis K. Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding. JMIR Formative Research 2022;6(1):e31623 View
  34. Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis. Genomics, Proteomics & Bioinformatics 2022;20(5):850 View
  35. Melo M, Santos P, Faustino E, Bastos-Filho C, Cerqueira Sodre A. Computational Intelligence-Based Methodology for Antenna Development. IEEE Access 2022;10:1860 View
  36. Xu L, Sanders L, Li K, Chow J. Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review. JMIR Cancer 2021;7(4):e27850 View
  37. Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clinical Chemistry and Laboratory Medicine (CCLM) 2022;60(12):1974 View
  38. Pagallo U, Bassi E, Durante M. The Normative Challenges of AI in Outer Space: Law, Ethics, and the Realignment of Terrestrial Standards. Philosophy & Technology 2023;36(2) View
  39. Jung J. Current Status and Future Direction of Artificial Intelligence in Healthcare and Medical Education. Korean Medical Education Review 2020;22(2):99 View
  40. Martin M, Kristeleit H, Ruta D, Karampera C, Hekmat R, Felix W, InHout B, Kothari A, Kazmi M, Ledwaba-Chapman L, Clery A, Wang Y, Coker B, Preininger A, Vergis R, Eggebraaten T, Gloe C, Irene I, Jackson G, Rigg A. Augmentation of a multidisciplinary team meeting with a clinical decision support system to triage breast cancer patients in the United Kingdom. Future Medicine AI 2023 View
  41. Kang C, Lee T, Lim W, Yeo W. Opportunities and challenges of 5G network technology toward precision medicine. Clinical and Translational Science 2023;16(11):2078 View
  42. Ural Y, Elter T, Yilmaz Y, Hallek M, Datta R, Kleinert R, Heidenreich A, Pfister D, Ayatollahi H. Validation and implementation of a mobile app decision support system for prostate cancer to improve quality of tumor boards. PLOS Digital Health 2023;2(6):e0000054 View
  43. Gandhi Z, Gurram P, Amgai B, Lekkala S, Lokhandwala A, Manne S, Mohammed A, Koshiya H, Dewaswala N, Desai R, Bhopalwala H, Ganti S, Surani S. Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes. Cancers 2023;15(21):5236 View
  44. Zhong N, Wang H, Huang X, Li Z, Cao L, Huo F, Liu B, Bu L. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Seminars in Cancer Biology 2023;95:52 View
  45. Panicker R, George A. Adoption of automated clinical decision support system: A recent literature review and a case study. Archives of Medicine and Health Sciences 2023;11(1):86 View
  46. Oehring R, Ramasetti N, Ng S, Roller R, Thomas P, Winter A, Maurer M, Moosburner S, Raschzok N, Kamali C, Pratschke J, Benzing C, Krenzien F. Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis. Frontiers in Oncology 2023;13 View
  47. Han C, Pan Y, Liu C, Yang X, Li J, Wang K, Sun Z, Liu H, Jin G, Fang F, Pan X, Tang T, Chen X, Pang S, Ma L, Wang X, Ren Y, Liu M, Liu F, Jiang M, Zhao J, Lu C, Lu Z, Gao D, Jiang Z, Pei J. Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment. Frontiers in Oncology 2023;13 View
  48. Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. Global Challenges 2024;8(1) View
  49. Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. Journal of Hematology & Oncology 2023;16(1) View
  50. Gao T, Ren H, He S, Liang D, Xu Y, Chen K, Wang Y, Zhu Y, Dong H, Xu Z, Chen W, Cheng W, Jing F, Tao X. Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol. Frontiers in Cardiovascular Medicine 2023;9 View
  51. Pagallo U, O’Sullivan S, Nevejans N, Holzinger A, Friebe M, Jeanquartier F, Jean-Quartier C, Miernik A. The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations. Health and Technology 2024;14(1):1 View
  52. Tan M, Xiao Y, Jing F, Xie Y, Lu S, Xiang M, Ren H. Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: a randomized controlled trial protocol. Frontiers in Psychiatry 2024;15 View
  53. Gairola S, Solanki S, Patkar S, Goel M. Artificial Intelligence in Perioperative Planning and Management of Liver Resection. Indian Journal of Surgical Oncology 2024 View
  54. Hendriks M, Jager A, Ebben K, van Til J, Siesling S. Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review. Critical Reviews in Oncology/Hematology 2024;195:104267 View

Books/Policy Documents

  1. Fiske A, Henningsen P, Buyx A. Ethics of Digital Well-Being. View
  2. Catania L. Foundations of Artificial Intelligence in Healthcare and Bioscience. View
  3. Molefi T, Marima R, Demetriou D, Basera A, Dlamini Z. Artificial Intelligence and Precision Oncology. View
  4. Vyas S, Bhargava D. Smart Health Systems. View
  5. Saeed H, El Naqa I. Machine and Deep Learning in Oncology, Medical Physics and Radiology. View
  6. Triberti S, Durosini I, La Torre D, Sebri V, Savioni L, Pravettoni G. Handbook of Artificial Intelligence in Healthcare. View
  7. Das S, Talukdar A, Nath D, Choudhury M. Computational Methods in Drug Discovery and Repurposing for Cancer Therapy. View
  8. AlZaabi A, Bouchareb Y, Mula-Hussain L. Artificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry. View