Todd R. McNutt

In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI’s dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on the longitudinal use of AI in a high-stakes workplace among cancer specialists. Initial operational gains hid “intuition rust”: the gradual dulling of expert judgment. These asymptomatic effects evolved into chronic harms, such as skill atrophy and identity commoditization. Building on these findings, we offer a framework for dignified Human-AI interaction co-constructed with professional knowledge workers facing AI-induced skill erosion without traditional labor protections. The framework operationalizes sociotechnical immunity through dual-purpose mechanisms that serve institutional quality goals while building worker power to detect, contain, and recover from skill erosion, and preserve human identity. Evaluated across healthcare and software engineering, our work takes a foundational step toward dignified human-AI interaction futures by balancing productivity with the preservation of human expertise.

Ultrasound imaging is a crucial tool for guiding radiation therapy, particularly for cancers such as pancreatic cancer, where tumors exhibit respiration-induced motion. While flexible ultrasound transducers offer improved anatomical conformity and reduced compression-induced distortion compared to rigid probes, their variable geometry presents significant challenges for conventional beamforming. In this study, we investigate a deep learning-based beamforming framework that directly predicts delayed RF data from raw RF input, bypassing explicit transducer shape estimation and traditional delay-and-sum computations. Building upon an artificial curvature simulation strategy, we systematically analyze the impact of curvature-induced variation and inherent RF noise on model performance and generalizability. We further introduce frequency-domain analysis to quantify RF-level signal variation that may not be apparent in spatial-domain image comparisons. Our results demonstrate that although noise-augmented training improves prediction consistency, reconstruction performance remains limited under the current prototype noise conditions. These findings highlight the importance of RF data diversity and noise characterization in developing clinically robust deep learning beamformers for flexible transducer-based ultrasound-guided radiation therapy.

In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI's dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on the longitudinal use of AI in a high-stakes workplace among cancer specialists. Initial operational gains hid ``intuition rust'': the gradual dulling of expert judgment. These asymptomatic effects evolved into chronic harms, such as skill atrophy and identity commoditization. Building on these findings, we offer a framework for dignified Human-AI interaction co-constructed with professional knowledge workers facing AI-induced skill erosion without traditional labor protections. The framework operationalizes sociotechnical immunity through dual-purpose mechanisms that serve institutional quality goals while building worker power to detect, contain, and recover from skill erosion, and preserve human identity. Evaluated across healthcare and software engineering, our work takes a foundational step toward dignified human-AI interaction futures by balancing productivity with the preservation of human expertise.

In many applied optimization settings, parameters that define the constraints may not guarantee the best possible solution, and superior solutions might exist that are infeasible for the given parameter values. Removing such constraints, re-optimizing, and evaluating the new solution may be insufficient, as the optimizer's preferences in selecting the existing solutions might be lost. To address this issue, we present an inverse optimization-based model that takes an observed solution as input and aims to improve upon it by projecting onto desired hyperplanes or expanding the feasible set while balancing the distance to the observed decision to preserve the optimizer's preferences. We demonstrate the applicability of the model in the context of radiation therapy treatment planning, an essential component of cancer treatment. Radiation therapy treatment planning is typically guided by expert-driven guidelines that define the optimization problem but remain mostly general. Our model provides an automated framework that learns new plans from available plans based on given clinical criteria, optimizing the desired effect without compromising the remaining constraints. The proposed approach is applied to a cohort of four prostate cancer patients, and the results demonstrate improvements in dose-volume histograms while maintaining comparable target coverage to clinically acceptable plans. By optimizing the parameters of the treatment planning problem and exploring the Pareto frontier, our methodology uncovers previously unattainable solutions that enhance organ-at-risk sparing without sacrificing target coverage. The framework's ability to handle multiple organs-at-risk and various dose-volume constraints highlights its flexibility and potential for application to diverse radiation therapy treatment planning scenarios.

Image-guided interventional oncology procedures can greatly enhance the outcome of cancer treatment. As an enhancing procedure, oncology smart material delivery can increase cancer therapy’s quality, effectiveness, and safety. However, the effectiveness of enhancing procedures highly depends on the accuracy of smart material placement procedures. Inaccurate placement of smart materials can lead to adverse side effects and health hazards. Image guidance can considerably improve the safety and robustness of smart material delivery. In this study, we developed a novel generative deep-learning platform that highly prioritizes clinical practicality and provides the most informative intra-operative feedback for image-guided smart material delivery. XIOSIS generates a patient-specific 3D volumetric computed tomography (CT) from three intraoperative radiographs (X-ray images) acquired by a mobile C-arm during the operation. As the first of its kind, XIOSIS (i) synthesizes the CT from small field-of-view radiographs;(ii) reconstructs the intra-operative spacer distribution; (iii) is robust; and (iv) is equipped with a novel soft-contrast cost function. To demonstrate the effectiveness of XIOSIS in providing intra-operative image guidance, we applied XIOSIS to the duodenal hydrogel spacer placement procedure. We evaluated XIOSIS performance in an image-guided virtual spacer placement and actual spacer placement in two cadaver specimens. XIOSIS showed a clinically acceptable performance, reconstructed the 3D intra-operative hydrogel spacer distribution with an average structural similarity of 0.88 and Dice coefficient of 0.63 and with less than 1 cm difference in spacer location relative to the spinal cord.

Lymphopenia is associated with decreased survival outcomes in head and neck squamous cell carcinoma (HNSCC). This is a significant concern especially for frail individuals who are more vulnerable to immunological dysregulations. A robust analysis is needed to understand whether we should expend efforts to limit treatment-related hematological toxicities in HNSCC. First, we would like to understand the prognostic significance for baseline (BL) vs. treatment-related lymphopenia (TRL). We want to know if lymphopenia is merely a reflection of the patient’s frailty at baseline, or if this is iatrogenic and therefore can be a target for treatment modifications in a bid to improve survival. Here, we identified 222 newly diagnosed M0 HNSCC patients treated with radiation ± systemic therapy in 2015-2018 at our institution. Clinical frailty is defined by age ≥ 65 or KPS ≤ 70. Using Kaplan Meier estimates and Cox regression with dichotomous variables, we performed multivariate analysis for both overall survival (OS) and progression-free survival (PFS). Important predictors included frailty (p ≤ 0.002) and ≥ 2K/mm³ decrease in absolute lymphocyte counts (ALC) (p ≤ 0.006). BL did not significantly impact OS or PFS (p ≤ 0.37). For TRL, ALC decrease had greater prognostic significance compared with ALC nadirs (p ≤ 0.45). Next, we simplified our survival model to include only frailty and TRL. Overall, fit patients with modest ALC decline (< 2K/mm³) achieved excellent survival outcomes while frail patients with severe ALC decline (≥ 2K/mm³) had inferior outcomes (3-year OS 95% vs. 56%, p < 0.0001; 3-year PFS 84% vs. 42%, p = 0.002). On subgroup analyses, similar outcomes were also identified for HPV+ HNSCC (n = 172, 3-year OS 96% vs. 71%, p = 0.002; 3-year PFS 89% vs. 54% p = 0.03). Finally, we want to know if ALC decline is determined primarily by treatment intensity, or if frail patients are naturally predisposed to greater ALC decline during treatment. In our data, frail patients had more modest ALC decline when compared to fit patients (1.26 ± 0.57 vs. 1.54 ± 0.58 K/mm³ p = 0.0006). In our survival model, we did not observe any first-order interactions between frailty and ALC decline (p = 0.54). The extent of ALC decline was also higher for patients with concurrent administrations of cisplatin (as opposed to radiation alone, p < 0.0001). Thus, we postulate that the extent of ALC decline is determined primarily by treatment intensity. It is possible that frail patients with severe ALC decline have inferior survival outcomes because they cannot maintain immunological balance and respond poorly when there is significant TRL. This opens opportunities to re-evaluate if the risk of TRL is modifiable by limiting the number of concurrent cycles of chemotherapy administered, and by considering radiation dose de-intensification especially for HPV-associated HNSCC. Lastly, TRL becomes an important consideration when immunotherapeutics are grafted onto existing CRT treatment paradigms. Citation Format: Carmen Kut, Todd McNutt, Carole Fakhry, Theodore DeWeese, Harry Quon. Lymphocyte kinetics, frailty and survival outcomes in HNSCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1971.

Rationale, Aims and Objectives: In the management of symptomatic bone metastases, current practice guidelines do not provide clear methodology for selecting palliative radiotherapy (RT) regimens based on specific patient and disease features. Decision support aids may offer an effective means for translating the complex data needed to render individualised treatment decisions, yet no such tools are available for use in this setting. Thus, we describe the development of the Bone Metastases Ensemble Trees for Survival‐Decision Support Platform (BMETS‐DSP), which aims to optimise selection of evidence‐based, individualised palliative RT regimens.Method: The Ottawa Decision Support Framework was used as the theoretical basis for development of BMETS‐DSP. First, we utilised stakeholder input and review of the literature to assess determinants underlying the provider decision. Based on this assessment and iterative stakeholder feedback, we developed the web‐based, provider‐facing BMETS‐DSP. Consistent with the underlying theoretical framework, our design also included assessment of decision quality using the International Patient Decision Aids Standards (IPDAS) certification checklist.Results: Stakeholder input and review of 54 evidence‐based publications identified the following determinants of the provider decision: estimated prognosis, characteristics of the target symptomatic lesion and the primary cancer type, consideration of alternative interventions, access to patient‐specific recommendations, and patient preferences. Based on these determinants, we developed the BMETS‐DSP that (1) collects patient‐specific data, (2) displays an individualised predicted survival curve, and (3) provides case‐specific, evidence‐based recommendations regarding RT, open surgery, systemic therapy, and hospice referral to aid in the decision‐making process. The finalised tool met IPDAS quality requirements. Preliminary results of a pilot assessment suggest impact of clinical outcomes.Conclusions: We describe the successful development of a provider‐facing decision support platform to aid in the provision of palliative RT in better alignment with patient and disease features. Impact of the BMETS‐DSP on decision outcomes will be further assessed in a randomised, controlled study.