Supplementary file 2_AI-enhanced oncology MDT 2.0: from multi-modal data synergy to value-

29 peer-reviewed studies from 2020 to 2026 synthesize evidence on AI integration into oncology multidisciplinary team decision-making. The review, authored by Shuang Liu and shared under a CC-BY-4.0 license, evaluates clinical efficacy and socioeconomic implications. It finds AI systems achieve 62-76% concordance with human tumor boards across multiple cancer types.

Use Cases

• Evaluating AI-human concordance rates in clinical oncology decisions based on the reported 62-76% agreement range.
• Assessing the potential for AI to standardize guideline-adherent treatment recommendations as highlighted in the review.
• Analyzing socioeconomic benefits like reduced preparation time and democratized access to expertise mentioned in the description.
• Identifying limitations of AI in handling complex, individualized patient cases as discussed in the findings.

Strengths

• Evidence synthesis is based on 29 peer-reviewed studies, providing a substantial foundation.
• The review covers a defined time period from 2020 to 2026, offering recent analysis.
• Findings include specific, quantified performance metrics like 62-76% AI-human concordance rates.

Limitations

• The dataset is a single 11.3 KB document, indicating very limited scope and no structured data for computational analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• The review's conclusions are based on synthesized literature; original data and methodology details are not provided.

Provenance

Source

figshare

Collection Method

Systematic review of published literature.

Time Range

2020–2026

Freshness

Last updated 2026-05-25 04:43:21; freshness should be verified.