Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care

Abstract

Background Rapid advances in science challenge the timely adoption of evidence‐based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI) application that can provide real‐time patient‐specific decision support. Materials and Methods The Oncology Expert Advisor (OEA) was designed to simulate peer‐to‐peer consultation with three core functions: patient history summarization, treatment options recommendation, and management advisory. Machine‐learning algorithms were trained to construct a dynamic summary of patients cancer history and to suggest approved therapy or investigative trial options. All patient data used were retrospectively accrued. Ground truth was established for approximately 1,000 unique patients. The full Medline database of more than 23 million published abstracts was used as the literature corpus. Results OEA's accuracies of searching disparate sources within electronic medical records to extract complex clinical concepts from unstructured text documents varied, with F1 scores of 90%–96% for non‐time‐dependent concepts (e.g., diagnosis) and F1 scores of 63%–65% for time‐dependent concepts (e.g., therapy history timeline). Based on constructed patient profiles, OEA suggests approved therapy options linked to supporting evidence (99.9% recall; 88% precision), and screens for eligible clinical trials on http://ClinicalTrials.gov (97.9% recall; 96.9% precision). Conclusion Our results demonstrated technical feasibility of an AI‐powered application to construct longitudinal patient profiles in context and to suggest evidence‐based treatment and trial options. Our experience highlighted the necessity of collaboration across clinical and AI domains, and the requirement of clinical expertise throughout the process, from design to training to testing. Implications for Practice Artificial intelligence (AI)‐powered digital advisors such as the Oncology Expert Advisor have the potential to augment the capacity and update the knowledge base of practicing oncologists. By constructing dynamic patient profiles from disparate data sources and organizing and vetting vast literature for relevance to a specific patient, such AI applications could empower oncologists to consider all therapy options based on the latest scientific evidence for their patients, and help them spend less time on information “hunting and gathering” and more time with the patients. However, realization of this will require not only AI technology maturation but also active participation and leadership by clincial experts.

Contributions on Clinical Decision Support from the 2018 Literature

“…Then, following the IMIA Yearbook best paper selection process, these 15 papers were rated by external reviewers and the Yearbook editors. Four papers were finally selected as best papers for 2018 5 6 7 8 ( Table 1 ). These are discussed in the next section, while summaries of their contents are available in the Appendix ofthis synopsis.…”

“…The third paper by Simon et al 7 is a case study report on the experience the authors gained in developing the Oncology Expert Advisor (OEA), an AI-based CDSS, and through its clinical assessment while introducing it to clinic team members. OEA has been built on top of the widely advertised IBM Watson technologies 9 and has been developed for the MD Anderson cancer center (Houston, TX, USA).…”

“…Among the four best papers, three implemented data-driven methods 5 6 7 , whereas one builds on a knowledge-based approach 8 . This is not inconsistent with the survey paper by Montani and Striani 1 , which remarked on the prevalence of data-driven AI over knowledge-based AI in the recent scientific literature related to decision support.…”

Artificial intelligence, regenerative surgery, robotics? What is realistic for the future of surgery?

“…In the next half century, there will be the rise of 4 key technologies: artificial intelligence, robotics, genomics and regenerative medicine. Artificial intelligence will build on the machine learning systems we currently have in place [ 13 ] to provide surgeons with accurate technical planning and management ability; much like a ‘second opinion’ [ 14 ]. It will also allow for rapid analysis of screening data in the preoperative and diagnostic environment [ 15 ].…”

Une plateforme multimodale d’aide à la décision. Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE

“…D'autres travaux utilisant des approches issues du web sémantique ont également été proposés, par exemple, Abidi et al (2007) ont proposé d'utiliser des ontologies pour guider un raisonnement basé sur des règles de production pour la prise en charge de patients atteints d'un cancer du sein. Plus récemment, des systèmes comme IBM Watson for Oncology (Lee et al, 2018) ou the Oncology Expert Advisor (Simon et al, 2018) cherchent à construire des outils d'aide à la décision en cancérologie s'appuyant sur des techniques d'intelligence artificielle apprenant sur les données rétrospectives de décisions de RCP. DESIREE est un projet européen financé dans le cadre du programme H2020 3 .…”