Regge, Daniele

Abstract Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Abstract The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

Purpose Few options are available for pain relief in patients with bone metastases who fail standard treatments. We sought to determine the benefit of radiofrequency ablation (RFA) in providing pain relief for patients with refractory pain secondary to metastases involving bone. Patients and Methods Thirty-one US and 12 European patients with painful osteolytic metastases involving bone were treated with image-guided RFA using a multitip needle. Treated patients had ≥ 4/10 pain and had either failed or were poor candidates for standard treatments such as radiation or opioid analgesics. Using the Brief Pain Inventory–Short Form, worst pain intensity was the primary end point, with a 2-unit drop considered clinically significant. Results Forty-three patients were treated (median follow-up, 16 weeks). Before RFA, the mean score for worst pain was 7.9 (range, 4/10 to 10/10). Four, 12, and 24 weeks following treatment, worst pain decreased to 4.5 (P < .0001), 3.0 (P < .0001), and 1.4 (P = .0005), respectively. Ninety-five percent (41 of 43 patients) experienced a decrease in pain that was considered clinically significant. Opioid usage significantly decreased at weeks 8 and 12. Adverse events were seen in 3 patients and included (1) a second-degree skin burn at the grounding pad site, (2) transient bowel and bladder incontinence following treatment of a metastasis involving the sacrum, and (3) a fracture of the acetabulum following RFA of an acetabular lesion. Conclusion RFA of painful osteolytic metastases provides significant pain relief for cancer patients who have failed standard treatments.