Table 1.

Topics, opportunities, and recommendation in cancer care.

Topic or section (references)	Opportunities	Recommendations
Patient engagement and participatory design [11-29]	Involvement of real users Identification of user needs Unique perspective on user acceptability, usability, and feasibility	Participatory design approach early and throughout the design process Focus groups with stakeholder representatives Fuse findings with those from other sources
Small data analytics [36]	Address the annotation problem via appropriate tools Enable experts to teach MLa models that automatically build and annotate their data sets Automatically represent knowledge in a structured and computerized way	Design new machine learning algorithms that needs minimal feedback from human experts Use knowledge-based learning that can be extended by data-driven findings easily and that uses standardized terminologies to provide interoperability and ease the updating and maintenance of the latest evidence
Integration and data management [42-50]	Exploiting aggregated, heterogeneous, and distributed data Translating the clinical findings into an intuitive representation for the patient Building individualized clinical recommendations based on the data interpretation projected in actions upon the patient Data completeness and augmentation	Embed continuous user feedback and iterative prototyping in the intervention Usage of tools to extract and represent the medical substrate by synthesizing only relevant aspects in a declarative way Development of clinical projections from individualized patient recommendations to therapy plans that embed temporal, procedural, and reasoning processes Incorporation of lived experiences of the patients
Extracting patient’s portrait [62-67]	Exploit correlations among multiple data sources to extract patient profile Use data mining and machine learning to guide therapeutic schemes Identify relevant genetic, phenotypical, physiological, lifestyle, and medical data correlations with diagnosis Provide an integrative approach to patient-centered data and demonstrate the potential of feature selection in data analysis and predictive patient-specific outcomes	Exploit available data Extract underlying correlations Integrate the multitude of representations in a structured object guiding therapeutic interventions in cancer care
Learning patient disease trajectory for personalized diagnosis [81-84]	Handling data and choosing specific latent variable models to summarize and extract information from the irregularly sampled and sparse data Learning of a disease trajectory is linked to the inherent computational complexity Continuous adaptation and update in face of disease progression heterogeneity Observed versus latent data artifacts	Use of discriminative models that exploit conditions on marker histories instead of jointly modeling them Focus on machine models which grow linearly in the number of marker types included in those models Use of a model capable of being applied dynamically in continuous time and updated Exploit models that account for latent factors and covariates influencing disease expression
Technological interventions in cancer rehabilitation [91-102]	Cope with patient sensory, motor, and cognitive deficit variability Identify therapy sequelae	Perform a precise assessment of patient’s sensory or motor or cognitive deficit variability Use machine learning algorithms to identify underlying correlations in patient data and generalize for robust prediction Exploit and mine large sets of structured and unstructured data to identify correlations and map to a certain type of dysfunction
Addressing current interoperability challenges [103-111,113-115]	Provide cancer-wide care Support diagnosis assistance for complex patients Provide a complete look at the patient’s medical history so physicians can see ineffective treatments Improve surveillance and research	Develop, test, disseminate, and adopt technical standards for information related to cancer care across the continuum Optimize the flow of information to serve the needs of caregivers, patients, and providers Develop and use standard, open application programming interfaces Promote incentives for the pooling of data and comparison of system-level research Support open use and sharing of big data, without compromising patients’ rights to privacy and confidentiality
Patient-clinician shared decision-making processes [122-129]	Inclusion of patient preferences during the decision making and tracking of their impact in the provided care services Identification of preference-sensitive decisions	Create a clear taxonomy (ie, systematic categorization) for patients’ preferences to serve as a standardization Harmonize different points of view to facilitate labeling and extraction of information in a processable and understandable way Build a methodology to synthesize knowledge
Assessment of clinical evidence-based recommendations, including PROMsb [130-133]	Quick access to latest available evidence Incorporate new sources of information that can support the decision-making process Increase the knowledge required by patients Effectively assess how good the treatment given was for each patient, not in the scope of a randomized control trial, but in the real-world environment	Explore new ways of including PROMs to assess guideline recommendations Exploit PROMs in the decision-making process, considering patient status reported by the patients themselves Consider and use existing quality assessment specifications for PROMs
Ambiguity on clinical guidelines used for clinical decision support [135-138]	Insight from complex clinical cases in a natural and intuitive way Patient-specific advice when and where needed	Promotion of standardized clinical terminology Integration of clinical guidelines with care flow
Up-to-date clinical evidence guidelines for CDSSc [144-146]	Create tools that support the easy updating of CIGsd for clinicians Interfaces that are easy to use and understand are required for this purpose for CIGs	Generate tools that enable the input of guideline information in an easy and visual manner and enable the modification of CIGs previously formalized in the system Provide a tool for detecting modifications on guidelines Semiautomate the formalization of guidelines using natural language processing
Trust and reliance on cancer care [148]	Assurance of the credibility of results generated by various computational tools available on the web	Provision of a certificate that discloses who is responsible and what tests are done or can be done to validate or test the trustworthiness of the output Include the versions of the data and the software in a report to help explain the deviation from the previous version
Trust in computer-aided diagnosis systems [149-152]	Increase confidence in the support provided by a CADe system	CAD support systems must embody reliable confidence measures as one of their key elements Incorporate trust into the initial classifier design when such algorithms are to be embedded into a cancer CAD system
Regulatory roadmap for validating the effectiveness of AIf-based models for clinical decision making [157-160]	Validation of AI in a safe and transparent way without compromising the potential of AI	Identify and define users, stakeholders, and use cases (data flows) Build regulatory frameworks aiming to provide guidance toward the validation or qualification of AI tools within different scenarios and pathways Consolidate input from scientific experts, health authorities, and published guidelines

^aML: machine learning.

^bPROM: patient-reported outcome.

^cCDSS: clinical decision support system.

^dCIG: computer-interpretable guideline.

^eCAD: computer-aided design.

^fAI: artificial intelligence.