Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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JMIR cancer
AI-Enhanced Predictive Analytics to Optimize Tele-Oncology Implementation in Rural Settings: Scoping Review
BACKGROUND: Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist in rural settings. AI-enhanced predictive analytics offer opportunities for optimizing deployment through personalized, data-driven strategies; however, evidence in rural tele-oncology contexts remains limited, and critical equity considerations remain underexamined. OBJECTIVE: This scoping review aimed to map evidence on AI-enhanced predictive analytics in tele-oncology implementation, with particular attention to rural and underserved populations, to identify research gaps and inform implementation science priorities. METHODS: We searched 5 databases (PubMed, Embase, CINAHL, Web of Science, and IEEE Xplore) using 4 concept domains (tele-oncology, rural implementation barriers, AI or predictive analytics, implementation science) from January 2015 through November 2025. Two independent reviewers screened 330 unique records (title or abstract; Cohen κ=0.78), with the principal investigator resolving conflicts. Of 138 full-text reviews (κ=0.82), 4 studies met inclusion criteria. Data extraction captured study characteristics, AI applications, implementation factors, and outcomes. We used narrative thematic analysis to map findings into three themes: (1) the current tele-oncology implementation landscape in rural and underserved settings, (2) potential AI applications addressing implementation challenges, and (3) implementation considerations for AI systems themselves. RESULTS: Four included studies (1 pilot feasibility study, 1 proof-of-concept validation study, 1 cross-sectional predictive study, and 1 platform development study; published 2019-2025) demonstrated limited evidence at the intersection of AI, tele-oncology, and rural health equity. Patient characteristics predicted telehealth modality preferences with 86.2% accuracy, revealing that male patients exhibited 66% increased odds of video selection versus female patients (P=.004), and urban residents showed 101% increased odds compared to rural counterparts (P<.001). Liu et al demonstrated that disadvantaged populations engaged with AI-generated health literacy content 2.52-fold more frequently than nondisadvantaged counterparts. However, all 4 studies documented substantial implementation barriers (patient, provider, organizational, and system levels) persisting despite technological sophistication. Organizational threshold effects, where remote monitoring interventions succeeded with adequate provider capacity but failed under resource constraints-suggest that algorithmic innovations cannot overcome structural limitations in rural facilities. No studies explicitly examined algorithmic bias, cross-population validation, or potential harms in rural contexts. Geographic concentration in high-resource countries (United States n=2, Greece n=1, and Singapore n=1) and limited oncology-specific focus underscore structural gaps in knowledge generation for underserved populations. CONCLUSIONS: Current evidence remains insufficient to support definitive practice recommendations. The observed evidence gap may reflect broader structural inequities in knowledge generation: populations with the greatest implementation challenges appear to remain substantially underrepresented in AI and digital health literature. Future research should prioritize comparative effectiveness studies in authentic rural contexts with implementation science outcomes, equity-centered cross-population validation, specification of translation mechanisms linking AI predictions to implementation strategies, health economic analyses, and mechanistic research on sociotechnical integration factors, ensuring technological innovation reduces rather than perpetuates disparities in cancer care.
19 July 2026
Read appraisal →BMJ supportive & palliative care
Time toxicity of patients with cancer enrolled in clinical trials: a systematic review and meta-analysis
PURPOSE: Time toxicity-the time patients spend receiving treatment, managing side effects, attending follow-ups and undergoing rehabilitation-is increasingly recognised as an important burden of cancer care. While time toxicity has been studied in routine clinical settings, it has rarely been quantified among patients with cancer enrolled in pharmacotherapy clinical trials. This systematic review aims to quantify time toxicity in this population. DESIGN: We searched Ovid MEDLINE, Embase and Cochrane CENTRAL from inception to 12 June 2025. Studies were included if they reported time toxicity among patients with cancer enrolled in clinical trials. Time toxicity was defined as calendar days with healthcare contact. Primary outcomes were (1) total days, (2) planned days and (3) unplanned days per month. Study quality was assessed using the Joanna Briggs Institute's (JBI) Checklist for Cohort Studies. RESULTS: Four retrospective studies reporting on an aggregate of 1809 patients were included. Two studies had low risk for bias and two had unclear risk of bias. Patients experienced time toxicity a mean of 7.6 days per month, with the highest burden in the first month of trial participation. Planned and unplanned days averaged 2.2 and 2.0 days per month, respectively. The ratio of planned to unplanned days remained consistent over time. CONCLUSIONS: Time toxicity is a substantial, under-recognised burden in cancer pharmacotherapy trials-approximately two times that experienced by non-participants. Its measurement and mitigation should be prioritised to improve patient-centred care, support shared decision-making and inform trial design, policy and consent processes. REGISTRATION: Study protocol preregistration: https://doi.org/10.17605/OSF.IO/BVY7C.
1 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
The effectiveness of non-pharmacological interventions on sleep quality in lung cancer patients: a systematic review and meta-analysis (2015-2025)
PURPOSE: Sleep disorders are highly prevalent among lung cancer patients and seriously affect their quality of life and mental health. This systematic review and meta-analysis aimed to assess the effects of non-pharmacological interventions on sleep quality, quality of life, and anxiety in lung cancer patients. METHODS: PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang Data, and VIP databases for studies published between 2015 and February 2025. The primary outcome was sleep quality, with quality of life and anxiety levels as secondary outcomes. We assessed the risk of bias using the Cochrane tool and synthesized data using meta-analysis. RESULTS: Twenty-four studies involving 2604 patients were included. Non-pharmacological interventions were compared with usual care, placebo, or standard oncology care. After excluding studies with inappropriate content and methods, 20 studies evaluated sleep quality, and 12 studies evaluated quality of life and anxiety levels, respectively. The quality of the evidence was low to moderate; the promotion results should be interpreted with caution, but they may have a positive effect on the sleep quality, quality of life, and anxiety levels of lung cancer patients. CONCLUSIONS: Diverse non-pharmacological interventions can improve sleep quality, quality of life, and anxiety in lung cancer patients. However, high-quality, rigorously designed randomized controlled trials are still needed to confirm these findings. Future research should focus on personalized interventions tailored to lung cancer patients' characteristics and integrate digital health interventions to better support clinical decision-making.
28 June 2026
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