Review Papers Claim by Claim

Visual review — "Artificial intelligence in breast oncology" (Mundinger 2025)

Visualize first — critical synthesis and actionable critique below. All claims are inline-cited to the manuscript and major primary studies it references.

Critical synthesis (visual-first, text-second)

1. Scope & strength: Mundinger 2025 aggregates up-to-date, high-profile real-world implementations (PRAIM, MASAI, ScreenTrustCAD, Ng et al.), historical experiences (Watson for Oncology) and research tools (AlphaFold), providing a broad landscape view but without systematic search methods or meta-analytic weighting — appropriate as a narrative perspective but limited for definitive effect-size claims Mundinger 2025 Narrative Review
2. Screens & workflows: real-world non-inferiority trials consistently show AI can safely triage many normal exams, reducing radiologist workload — but evidence of improved patient-level outcomes (reduced interval cancers, mortality) remains pending longer follow-up and prospective outcome trials Eisemann et al. PRAIM trial
3. Decision support & LLMs: author rightly highlights concordance but also high error rates/"hallucinations" in complex cases: Watson's mixed record and ChatGPT concordance ~50–80% support the paper's cautionary stance and need for human oversight Somashekhar et al. Watson concordanceSorin et al. LLMs in breast cancer
4. Research & biomarker discovery: paper correctly credits AI (e.g., AlphaFold) for accelerating molecular discovery; that domain is high-value and research-ready — but translation to routine clinical benefit requires validation pipelines, regulatory paths, and reproducibility checks Mei et al. PCSK9 germline variant (Cell 2024)Malhotra et al. AlphaFold review
5. Ethics, energy, governance: the paper's attention to dataset quality, bias, transparency, liability, and energy consumption is appropriate. Energy numbers cited are rough estimates and should be contextualized with model-size, hardware and amortized training vs inference costs; the policy emphasis is correct but needs quantified life-cycle assessments for decisions at scale EU Parliament AI risks report

Practical recommendations (what the paper says + critical additions)

• Prioritize prospective outcome trials (not just non-inferiority screening accuracy) that measure interval cancers, stage-shift, harms, and cost-effectiveness over multiple years Hill et al. cost-effectiveness modeling.
• Require transparent reporting: data provenance, label quality, drift-detection, external validation, and equity audits (by age, density, ancestry) before large-scale deployment Label-noise study.
• Embed human-in-the-loop governance: limit autonomous action to low-risk triage; maintain clinician final decision authority especially in complex multimodal cases (genomics, multi-lesion disease).
• Invest in shared, curated datasets (e.g., MAMA-MIA DCE-MRI) and open benchmarks to reduce vendor lock-in and improve reproducibility MAMA-MIA dataset (Scientific Data 2025).

• Mundinger 2025 Narrative Review
• Eisemann et al. PRAIM
• Hernström et al. MASAI
• Somashekhar et al. Watson concordance
• MAMA-MIA dataset
• Label-noise preprint