Hadjiiski, Lubomir M.

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.

AbstractContextInhibition of the neonatal fragment crystallizable receptor (FcRn) reduces pathogenic thyrotropin receptor antibodies (TSH-R-Ab) that drive pathology in thyroid eye disease (TED).ObjectiveWe report the first clinical studies of an FcRn inhibitor, batoclimab, in TED.DesignProof-of-concept (POC) and randomized, double-blind placebo-controlled trials.SettingMulticenter.ParticipantsPatients with moderate-to-severe, active TED.InterventionIn the POC trial, patients received weekly subcutaneous injections of batoclimab 680 mg for 2 weeks, followed by 340 mg for 4 weeks. In the double-blind trial, patients were randomized 2:2:1:2 to weekly batoclimab (680 mg, 340 mg, 255 mg) or placebo for 12 weeks.Main OutcomeChange from baseline in serum anti-TSH-R-Ab and total IgG (POC); 12-week proptosis response (randomized trial).ResultsThe randomized trial was terminated because of an unanticipated increase in serum cholesterol; therefore, data from 65 of the planned 77 patients were analyzed. Both trials showed marked decreases in pathogenic anti-TSH-R-Ab and total IgG serum levels (P < .001) with batoclimab. In the randomized trial, there was no statistically significant difference with batoclimab vs placebo in proptosis response at 12 weeks, although significant differences were observed at several earlier timepoints. In addition, orbital muscle volume decreased (P < .03) at 12 weeks, whereas quality of life (appearance subscale) improved (P < .03) at 19 weeks in the 680-mg group. Batoclimab was generally well tolerated, with albumin reductions and increases in lipids that reversed upon discontinuation.ConclusionsThese results provide insight into the efficacy and safety of batoclimab and support its further investigation as a potential therapy for TED.