Shared Decision Making, Decision Aids and Patient Reported Outcome Measures for Overactive Bladder Care: A Review

Development of National Research and Clinical Agendas for Patient-Reported Outcomes in IR: Proceedings from a Multidisciplinary Consensus Panel

Response to “Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine”

Anyone with access to the internet now has free access to artificial intelligence (AI) applications that can quickly develop text-based responses to specific questions. Large language model applications such as ChatGPT have made it possible to write research manuscripts, abstracts, and letters to the editor that are extremely difficult to differentiate from human-derived work (see Appendix; https://links.lww.com/CORR/B99). This rapid improvement in AI capabilities may offer some benefits to journals, publishers, readers, and, ultimately, patients. For example, large language models such as ChatGPT might—with suitable human oversight—be able to create plain-language summaries of complex research quickly and at scale, which might make the scientific record more accessible to the public [6]. AI-based tools also may facilitate the creation of consistent, clear visual presentations of complex data. And, of course, an exciting feature of transformative technologies is the potential for benefits that we cannot imagine at the outset. However, misuse of these tools can undermine the integrity of the scholarly record; indeed, there are examples of this happening already. Some even have suggested that large language models should be considered authors. In fact, ChatGPT has been listed as a co-author in published research [4] and even is a registered author in the ORCiD and SCOPUS databases. This practice is inappropriate. Under the authorship guidelines of the International Committee of Medical Journal Editors [3], which all of our journals follow, an author must meet a number of important standards, including being willing to be accountable for all aspects of the work, to ensure that questions related to the accuracy or integrity of the work will be suitably investigated and resolved, to be able to identify which co-authors are responsible for specific parts of the work, and to have confidence in the integrity of the contributions of their co-authors. A large language model has no means to comply with such standards, and, for that reason—as well as, we believe, simple common sense—AI-based tools cannot be authors on scientific papers. Other important concerns have been raised about the use of AI-driven tools in scientific reporting, including the possibilities that they may produce material that is inaccurate or out of date [2], they may conjure up “sources” that do not exist [1], and—this from the team that built ChatGPT—they may generate “plausible-sounding but incorrect or nonsensical answers,” which the coders have said is “challenging” to fix because “during RL (reinforcement learning) training, there’s currently no source of truth” [5]. We believe that our readers, and the patients for whom they are responsible, deserve better. For these reasons and others, our editorial boards have agreed on the following standards concerning AI applications that create text, tables, figures, images, computer code, and/or video: 1. AI applications cannot be listed as authors. 2. Whether and how AI applications were used in the research or the reporting of its findings must be described in detail in the Methods section and should be mentioned again in the Acknowledgments section. Our editorial boards will closely follow the scientific developments in this area and will adjust editorial policy as frequently as required.

BackgroundDecision aids educate patients about treatment options and outcomes. Communication aids include question lists, consultation summaries, and audio-recordings. In efficacy studies, decision aids increased patient knowledge, while communication aids increased patient question-asking and information recall. Starting in 2004, we trained successive cohorts of post-baccalaureate, pre-medical interns to coach patients in the use of decision and communication aids at our university-based breast cancer clinic.MethodsFrom July 2005 through June 2012, we used the RE-AIM framework to measure Reach, Effectiveness, Adoption, Implementation and Maintenance of our interventions.ResultsReach: Over the study period, our program sent a total of 5,153 decision aids and directly administered 2,004 communication aids. In the most recent program year (2012), out of 1,524 eligible patient appointments, we successfully contacted 1,212 (80 %); coached 1,110 (73 %) in the self-administered use of decision and communication aids; sent 958 (63 %) decision aids; and directly administered communication aids for 419 (27 %) patients. In a 2010 survey, coached patients reported self-administering one or more communication aids in 81 % of visitsEffectiveness: In our pre-post comparisons, decision aids were associated with increased patient knowledge and decreased decisional conflict. Communication aids were associated with increased self-efficacy and number of questions; and with high ratings of patient preparedness and satisfactionAdoption: Among visitors sent decision aids, 82 % of survey respondents reviewed some or all; among those administered communication aids, 86 % reviewed one or more after the visitImplementation: Through continuous quality adaptations, we increased the proportion of available staff time used for patient support (i.e. exploitation of workforce capacity) from 29 % in 2005 to 84 % in 2012Maintenance: The main barrier to sustainability was the cost of paid intern labor. We addressed this by testing a service learning model in which student interns work as program coaches in exchange for academic credit rather than salary. The feasibility test succeeded, and we are now expanding the use of unpaid interns.ConclusionWe have sustained a clinic-wide implementation of decision and communication aids through a novel staffing model that uses paid and unpaid student interns as coaches.

Leveraging Outcomes Research to Optimize Prostate Cancer Care

In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.

The recent study by Kafetzis et al. on AI-assisted Hill classification represents a significant advancement in the standardization of gastroesophageal junction (GEJ) assessment [1]. The importance of accurate GEJ evaluation cannot be overstated, particularly in the context of gastroesophageal reflux disease (GERD) and Barrett's esophagus. The Hill grade classification, an endoscopic classification of the anti-reflux barrier focused on the flap valve, has been reported to correlate with the severity of GERD. However, clinical utilization of this classification has been limited mainly due to inter-observer variability [2, 3]. The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction in the past decade. Particularly in the field of upper gastrointestinal endoscopy, the application of AI has been focused on the detection and characterization of pre-malignant and malignant lesions, and this work thus represents a part of the recent shift toward addressing this imbalance [4-6]. As we evaluate this study and contemplate future directions, it is crucial to consider the recent guidelines set forth by QUAIDE (Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy) [7]. This framework provides valuable guidance for the design, reporting, and interpretation of AI studies in gastrointestinal endoscopy. The work by Kafetzis et al. aligns well with several QUAIDE recommendations, particularly in its patient-based selection for test sets and multi-annotator approach. The real-time implementation and explainability features of their AI system are particularly commendable. These aspects not only enhance the system's potential for clinical integration but also address the need for transparency in AI-assisted medical decision-making. Looking beyond the immediate implications of this study, we can envision a future where AI-assisted Hill classification serves as a foundation for a comprehensive GEJ assessment. The integration of endoscopic imaging with the data from the high-resolution esophageal manometry, PH studies, and EndoFlip could potentially provide a multidimensional view of GEJ function. Furthermore, the combination of Computer Vision with symptom analysis Large Language Models (LLMs) presents an exciting opportunity to bridge the gap between endoscopic findings and patient-reported outcomes, aligning with the growing emphasis on patient-centered care in gastroenterology [8]. The potential for AI to predict outcomes of anti-reflux interventions is another avenue worthy of exploration. By synthesizing Hill grade classification with other patient-related clinical parameters, AI could potentially optimize patient selection in an attempt to improve clinical success rates and patient outcomes [3]. As we contemplate these possibilities, it is crucial to recognize the importance of clinician involvement in AI development. Physician-led initiatives, such as this study, supported by appropriate regulatory frameworks and funding, are essential to ensure that AI systems remain clinically relevant and easily adoptable. The single-center design of the study underscores the necessity for multi-institutional validation to ensure generalizability across diverse clinical settings. In this context, federated learning emerges as a promising approach to facilitate large-scale, multi-institutional studies while navigating the complexities of data privacy in healthcare. This approach could be particularly valuable in addressing the challenges of data sharing and collaboration [9]. The integration of AI-assisted Hill classification into clinical practice could have profound implications for quality assurance and training in endoscopy. By providing objective, standardized assessments, it could serve as a valuable quality metric and revolutionize the training of new endoscopists [10]. In conclusion, the work by Kafetzis et al. represents not just an advancement in GEJ assessment, but a steppingstone toward a new paradigm in gastroenterology. It invites us to envision a future where AI-enhanced, multimodal approaches lead to more precise, personalized, and effective patient care. As we pursue this vision, close collaboration between clinicians, researchers, and technologists will be crucial in realizing the full potential of AI in our field. Nasim Parsa: Speaker for Phathom Pharmaceuticals, VP Medical Affairs at Satisfai Health Inc. Cem Simcek and Lumir Kunovsky have no disclosures. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

The Role of Patient-reported Outcomes and Medication Adherence Assessment in Patient-focused Drug Development for Solid Organ Transplantation.

Artificial intelligence (AI) applications for clinical genetics hold the potential to improve patient care through supporting diagnostics and management as well as automating administrative tasks, thus enhancing and potentially enabling clinician/patient interactions. While the introduction of AI into clinical genetics is increasing, there remain unclear questions about risks and benefits, and the readiness of the workforce. To assess the current clinical genetics workforce's use, knowledge, and attitudes toward available medical AI applications, we conducted a survey involving 215 US-based genetics clinicians and trainees. Over half (51.2%) of participants report little to no knowledge of AI in clinical genetics and 64.3% reported no formal training in AI applications. Formal training directly correlated with self-reported knowledge of AI in clinical genetics, with 69.3% of respondents with formal training reporting intermediate to extensive knowledge of AI vs. 37.5% without formal training. Most participants reported that they lacked sufficient knowledge of clinical AI (83.4%) and agreed that there should be more education in this area (97.6%) and would take a course if offered (89.3%). The majority (51.6%) of clinician participants said they never used AI applications in the clinic. However, after a tutorial describing clinical AI applications, 75.8% reported some use of AI applications in the clinic. When asked specifically about clinical AI application usage, the majority of clinician participants used facial diagnostic applications (54.9%) and AI-generated genomic testing results (62.1%), whereas other applications such as chatbots, large language models (LLMs), pedigree or medical summary generators, and risk assessment were only used by a fraction of the clinicians, ranging from 11.1 to 12.5%. Nearly all participants (94.6%) reported clinical genetics professionals as being overburdened. Further clinician education is both desired and needed to optimally utilize clinical AI applications with the potential to enhance patient care and alleviate the current strain on genetics clinics.

Introduction Patient-reported outcome measures (PROMs), including patient-reported outcomes (PROs), are one of two primary sources of data about patients on dialysis (1); the other is biologically based patient data. The Food and Drug Administration definition of a PRO is “any report coming from patients about a health condition and its treatment, without interpretation of the patient’s response by a clinician or anyone else” (2). We argue that this definition fits PROMs more generally and that PROs are a subset of that. Like other fields of medicine, in dialysis, PROMs are used as quality assessment and performance measures. Incorporating PROMs into clinical practice across medicine improves outcomes, such as patients’ survival (3). In a meta-analysis, 65% of studies provided evidence that PROMs improved processes of care (e.g., patient education and diagnoses), 47% of studies provided evidence that PROMs improved the outcomes of care (e.g., functional status), and 42% of studies provided evidence that PROMs improved satisfaction with care (4). Because the Centers for Medicare and Medicaid Services (CMS) pays for the cost of dialysis for the vast majority of patients with ESRD, they have a significant stake in understanding the quality of that care and its outcomes. The CMS is particularly interested in patient experience with care and health-related quality of life (HRQOL) and has codified recommendations or requirements that these PROMs be collected on all patients on dialysis. This paper outlines the major methodologic recommendations around use of PROMs in dialysis that we generated in a white paper commissioned by the Kidney Care Quality Alliance (KCQA). These recommendations were generated through a systematic review of the PROM literature and include (1) continue the use of the Kidney Disease Quality of Life 36-item version (KDQOL-36) for dialysis centers’ internal quality improvement activities and the In-Center Hemodialysis Consumer Assessment of Health Care Providers and Systems (ICH-CAHPS) measures for public dialysis center performance monitoring but promote efforts to modify these instruments by incorporating Patient Reported Outcomes Measurement Information System (PROMIS) general health items (KDQOL-36) and reducing the length of the ICH-CAHPS, (2) adopt a PROM of whether patients on dialysis have been informed about their option for transplant and all dialysis options, (3) evaluate equivalence between electronic and paper versions of PROMs before widespread use of electronic administration, (4) explore reimbursement of costs of PROM administration and training, and (5) continue development of provider trainings in PROM administration and interpretation (Table 1). These recommendations were made to the KCQA on the basis of our review and research into methodologic challenges around the use of PROMs in dialysis. Table 1. - Recommendations for use of patient-reported outcome measures in dialysis centers Category Recommendations Selection of PROMs Continue the use of the KDQOL-36 for dialysis centers’ internal quality improvement activities and the ICH-CAHPS for public dialysis center performance monitoring but promote efforts to modify these instruments by incorporating PROMIS general health items (KDQOL-36) and reducing the length of the ICH-CAHPS Adopt a PROM of whether patients have been informed about their option for transplant and all dialysis options Mode of administration Evaluate equivalence between electronic and paper versions of PROMs before widespread use of electronic administration Support for PROM use Explore reimbursement of costs and support for training for PROM administration from the CMS, the ESRD Networks, or professional societies Continue development of provider trainings in PROM administration and interpretation PROM, patient-reported OUTCOME measure; KDQOL-36, Kidney Disease Quality of Life 36-item version; ICH-CAHPS, In-Center Hemodialysis Consumer Assessment of Health Care Providers and Systems; PROMIS, Patient Reported Outcomes Measurement Information System; CMS, Centers for Medicare and Medicaid Services. Recommendation 1 Two of the most commonly used PROM instruments in dialysis facilities are the KDQOL-36 (5) and the ICH-CAHPS (6). The KDQOL-36 is the measure of choice for the CMS’s requirement of annual HRQOL assessment among all patients on dialysis. The ICH-CAHPS is mandated to be assessed twice annually by all patients on dialysis and is included as a clinical measure in the payment year 2019 Quality Improvement Program (QIP). Both of these instruments were developed with extensive patient and expert input, helping ensure that they represent the views and experiences of patients on dialysis and providers (5,6). In addition, support for the reliability (e.g., internal consistency reliability ≥0.80) and validity of the KDQOL-36 has been evidenced (7). Support for the reliability and validity of the ICH-CAHPS has also been presented (6). Finally, both of these measures have been administered to thousands of patients on dialysis, making possible clinically meaningful comparisons of individual patients with national and state norms and key clinical subgroups. As noted above, the ICH-CAHPS is administered as part of the CMS’s QIP. The KDQOL-36 is often administered to help meet the CMS’s requirement for annual quality of life assessment by vendors, like the Medical Education Institute, which administer the KDQOL-36 to thousands of patients on dialysis yearly. Considering these advantages, we recommend the continued use of the KDQOL-36 instrument with patients on dialysis for the purposes of dialysis centers’ internal quality improvement and the continued use of the ICH-CAHPS for the CMS’s dialysis center performance monitoring. There are opportunities to improve both of these measures. The KDQOL-36 incorporates the Medical Outcomes Study 12 Item Short Form Health Survey (SF-12) as its generic HRQOL core. However, the National Institutes of Health PROMIS measures are the state of the science in generic HRQOL measurement (8) and suitable as a replacement for the SF-12. In head to head comparisons, the PROMIS measures have shown better reliability than legacy measures, like the SF-12. The ICH-CAHPS composites could be made more parsimonious by using an approach similar to that used for the Consumer Assessment of Health Care Providers and Systems clinician and group survey, resulting in shorter surveys (9). Recommendation 2 In addition to HRQOL and patient experience, there are many other PROMs that provide relevant information about patients on dialysis. The decision making of patients with ESRD about their treatment is one domain where the use of PROMs in dialysis centers should be expanded. Patients with ESRD have multiple types of dialysis from which they may choose. In addition to dialysis, they may choose to pursue a living or deceased donor kidney transplant. All of these treatment options vary in the length and quality of additional life-years that they offer to patients (10). The importance of providing information about transplants to patients is evidenced by the fact that it increases the likelihood that they will pursue and receive transplants (11). For this reason, the CMS’s 2008 Conditions for Coverage for dialysis facilities require that information about the option for kidney transplant be provided to each patient on dialysis. However, patients on dialysis report having received information about transplant less than their providers report giving transplant information, indicating that provider reports may not be as accurate for this purpose (12). Additionally, there is evidence that alternative dialysis options may improve patients’ survival and HRQOL (10). It has been argued recently that, when patients on dialysis are not given access to information about the risks and benefits of all their treatment options, they cannot make informed consent for their dialysis treatment (13). We contend that patient reports of receiving information about their treatment options may be better indicators of whether informed decision making and consent around treatment choices actually occur among patients on dialysis compared with provider reports. Therefore, we recommend that the CMS adopt a PROM of whether patients on dialysis have been informed about their option for transplant and all of their dialysis options. Recommendation 3 A major methodologic challenge faced by dialysis facilities is implementing the best mode of survey administration. The International Society of Quality of Life Research reviewed the resources needed and tradeoffs associated with different modes of administration of PROMs (14), including self-, interviewer-, and computer-administered surveys given in the clinic, by mail, over the telephone, and electronically via the web. All of these options involve a balance of advantages, disadvantages, and resource inputs, each of which is detailed in our full manuscript (J.D. Peipert, R.D. Hays, unpublished manuscript). However, one mode of administration with expanding potential, electronically based PROM surveys, deserves special attention. Electronic administration, either on a computer or portable technologies like tablets, may offer attractive efficiencies over the other modes. One particularly attractive benefit of web-based surveys is the ability to input data into a database directly, avoiding potential problems with data entry. Many PROM instruments were originally developed to be administered in a paper/pencil format. Although these instruments likely do not need to be redeveloped for electronic administration, additional testing for equivalence should be conducted to determine if smaller modifications are required (e.g., updates to instructions and formatting or minor wording changes). Therefore, we recommend that new studies evaluate equivalence between electronic and paper versions of PROMs before widespread use of electronic administration. Additionally, inquiries into the challenges of this mode of administration for older adults, the frail, and those without high levels of technology literacy should be made before large-scale rollout. Recommendation 4 Another major challenge facing dialysis facilities around administering PROMs regards their financial and human resource costs. Administering PROMs requires significant staff time and expertise as well as material costs. Many dialysis staff, who are primarily responsible for administering PROMs to patients, already have a high workload. Along with data entry, interpretation of PROMs’ results and incorporation of these results into clinical intervention are expensive and difficult to accomplish without significant discretionary spending and resource investment (1). Therefore, we recommend that efforts be undertaken to explore reimbursement of costs and support for training for PROM administration from the CMS, the ESRD Networks, or professional societies. Recommendation 5 Related to recommendation 4, an important practical challenge faced in administering standardized PROM instruments in dialysis clinics regards the expertise required to properly administer them. The dialysis providers and staff administering PROMs in face to face or telephonic interviews require a special skill set, including the abilities to gather accurate responses, help patients with their questions and concerns without biasing their responses, execute complex skip patterns, and detect when patients may be giving untruthful responses. PROMs implemented through self-administered surveys (e.g., mailed to the patients) also require expertise, including the ability to execute standardized data entry protocols. These skills are not likely part of the training of many dialysis providers and staff, and therefore, additional training is often required. We recommend the continued development of provider trainings in PROM administration and interpretation to help dialysis providers build these skills. These trainings should target dialysis organizations to help their dialysis providers (e.g., nephrologists) and staff members (e.g., nurses and social workers) sharpen their ability to administer PROMs in clinic. In conclusion, dialysis payers, administrators, providers, and staff deserve recognition for their considerable efforts and successes in incorporating PROMs into routine care. However, there are still many challenges facing dialysis facilities around administering PROMs to their patients. We have identified multiple practical specific recommendations to assist in facing these challenges. These recommendations are intended to help dialysis care decision makers, clinicians, and applied researchers continue to improve the excellent track record of PROM use. Disclosures The authors were compensated by the Kidney Care Quality Alliance (KCQA) to prepare a white paper on methodologic issues around using patient-reported measures in dialysis. The recommendations presented in this manuscript represent the results of the authors’ research but do not necessarily represent the views of the KCQA. R.D.H. was among the team of investigators who originally developed Kidney Disease Quality of Life 36-item version and the In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems. Neither investigator benefits financially from the use of these measures.

Anyone with access to the Internet now has free access to artificial intelligence (AI) applications that can quickly develop text-based responses to specific questions. Large language model applications such as ChatGPT have made it possible to construct research manuscripts, abstracts, and letters to the editor that are extremely difficult to differentiate from human-derived work (Supporting Information: Appendix). This rapid improvement in AI capabilities may offer some benefits to journals, publishers, readers, and, ultimately, patients. For example, large language models such as ChatGPT might—with suitable human oversight—be able to create plain-language summaries of complex research quickly and at scale, which might make the scientific record more accessible to the public.1 AI-based tools also may facilitate the creation of consistent, clear visual presentations of complex data. And, of course, an exciting feature of transformative technologies is the potential for benefits that we cannot imagine at the outset. However, misuse of these tools can undermine the integrity of the scholarly record; indeed, there are examples of this happening already. Some have suggested that large language models should be considered authors; in fact, ChatGPT has been listed as a coauthor in published research2 and even is a registered author in the ORCiD and SCOPUS databases. This practice is inappropriate. Under the authorship guidelines of the International Committee of Medical Journal Editors, which all of our journals follow, an author must meet a number of important standards, including being willing to be accountable for all aspects of the work, to ensure that questions related to the accuracy or integrity of the work will be suitably investigated and resolved, to be able to identify which co-authors are responsible for specific parts of the work, and to have confidence in the integrity of the contributions of their coauthors.3 A large language model has no means to comply with such standards, and, for that reason—as well as, we believe, simple common sense—AI-based tools cannot be authors on scientific papers. Other important concerns have been raised about the use of AI-driven tools in scientific reporting, including the possibilities that they may produce material that is inaccurate or out of date,4 they may conjure up “sources” that do not exist,5 and—this from the team that built ChatGPT—they may generate “plausible-sounding but incorrect or nonsensical answers,” which the coders have said is “challenging” to fix because “during RL [reinforcement learning] training, there's currently no source of truth.”6 We believe that our readers, and the patients for whom they are responsible, deserve better. AI applications cannot be listed as authors. Whether and how AI applications were used in the research or the reporting of its findings must be described in detail in the Methods section, and should be mentioned again in the Acknowledgments section. Our editorial boards will closely follow the scientific developments in this area and will adjust editorial policy as frequently as required. All ICMJE Disclosure of Potential Conflicts of Interest forms for Clinical Orthopaedics and Related Research Editors are on file with the publication and can be viewed on request; the Editors’ disclosure statements also appear each month in print on the masthead of Clinical Orthopaedics and Related Research. The ICMJE Disclosure form for the Editor of The Bone & Joint Journal is available with the BJJ online version of this article. The ICMJE Disclosure form for the Editor of the Journal of Orthopaedic Research is available from the Orthopaedic Research Society. The ICMJE Disclosure form for the Editor of The Journal of Bone and Joint Surgery is provided with the JBJS online version of this article. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Artificial intelligence (AI) shows promise in streamlining MRI workflows by reducing radiologists' workload and improving diagnostic accuracy. Despite MRI's extensive clinical use, systematic evaluation of AI-driven productivity gains in MRI remains limited. This review addresses that gap by synthesizing evidence on how AI can shorten scanning and reading times, optimize worklist triage, and automate segmentation. On 15 November 2024, we searched PubMed, EMBASE, MEDLINE, Web of Science, Google Scholar, and Cochrane Library for English-language studies published between 2000 and 15 November 2024, focusing on AI applications in MRI. Additional searches of grey literature were conducted. After screening for relevance and full-text review, 67 studies met inclusion criteria. Extracted data included study design, AI techniques, and productivity-related outcomes such as time savings and diagnostic accuracy. The included studies were categorized into five themes: reducing scan times, automating segmentation, optimizing workflow, decreasing reading times, and general time-saving or workload reduction. Convolutional neural networks (CNNs), especially architectures like ResNet and U-Net, were commonly used for tasks ranging from segmentation to automated reporting. A few studies also explored machine learning-based automation software and, more recently, large language models. Although most demonstrated gains in efficiency and accuracy, limited external validation and dataset heterogeneity could reduce broader adoption. AI applications in MRI offer potential to enhance radiologist productivity, mainly through accelerated scans, automated segmentation, and streamlined workflows. Further research, including prospective validation and standardized metrics, is needed to enable safe, efficient, and equitable deployment of AI tools in clinical MRI practice.

Objective: We systematically created pamphlet and video decision aids (DAs) for destination therapy left ventricular assist device (DT LVAD) and hosted them online for free public use. Although DAs have been shown to improve patient knowledge and satisfaction, they are rarely used. Thus, we aimed to assess 1) if our DAs were being used in clinical practice and 2) evaluate the factors influencing uptake. Methods: We contacted people who previously requested information about our DAs. Through snowball sampling, we identified additional programs that may be using them. Participants completed a semi-structured interview. We analyzed the interviews using normalization process theory (NPT). Results: From May 2014 to November 2016, 30 people (surgeons, cardiologists, social workers, coordinators, nurses, industry managers) from 25 different organizations contacted our research team inquiring about use of the DAs. Nearly all were contacted for an interview (1 excluded for involvement in efficacy trial), with an additional 8 referred through snowballing. Of the 37 eligible, 28 people from 25 different organizations across the United States, Canada, and France participated. We found that 11 organizations currently use the DAs, 5 plan to use them but have not yet, and 9 do not currently use them nor have active plans to use them in the near future. All interviewees agreed that LVAD is a complex decision which requires as much knowledge transfer as possible. Using the 4 constructs of NPT—coherence, cognitive participation, collective action, reflexive monitoring—we found that the DAs were seen either as “good” educational resources, or as an important element of the education process that helped improve decision making (coherence). LVAD coordinators and social workers were typically the ones to use the DAs directly with patients; however, initial implementation at the program was done by a champion of the DAs, which was either the coordinator or social worker themselves or physicians who advocated for their use (cognitive participation). Use of the materials ranged from merely providing the pamphlet to utilizing both the pamphlet and the video as the cornerstone of teaching for all patients considering LVAD (collective action). Those using the DAs reported value and benefits to their use; some who did not use the DAs wished they could but were limited by systemic or programmatic issues, while those who did not plan to use them reported a lack of time, personnel, and resources as the reason for not implementing (reflexive monitoring). Conclusion: Members of many LVAD centers around the world independently inquired about freely available DT LVAD DAs, and over half of those centers are actively or in the process of implementing the DAs into routine care. Implementation was facilitated by unmet informational needs, invested clinical champions, a favorable environment, and successful experiences with the DAs.

Background: Neoadjuvant systemic therapy (NAST) is a treatment option for selected patients with highly proliferative and/or large operable breast cancer. Whilst survival outcomes are equivalent between up-front surgery and NAST, the decision about treatment sequence can be difficult due to complexity and perceived urgency of the decision. Patients may value the outcomes of these options, such as down staging and prognostication, differently. Involving patients in decisions about their healthcare reduces anxiety, increases quality of life and satisfaction with care. Decision aids can improve patient involvement in health care decisions, but one is not available for the decision about NAST. Aims/Methods: We conducted a prospective, single-arm pre-post study to evaluate a custom-designed decision aid developed for women who have been offered NAST. Eligible patients were: female; aged ≥18 years; diagnosed with an operable invasive breast cancer; considered for NAST with curative intent. Here, we report on the grounded theory qualitative analysis of a convenience sample of 16 semi-structured phone interviews to explore patient experience with this decision aid. Results: Participants' median age was 52 (IQR=41-63), median time since breast cancer diagnosis was 5 months (IQR=2-8). Most were married or living with a partner (81.3%) and had a University level degree (68.8%). Patients perceived the decision aid to be useful for becoming more informed and involved in deciding on NAST. Specifically, the decision aid enhanced patients' understanding of their type of breast cancer and the treatment options available to them by summarising and extending the information they received during the consultation with their doctor. Some women perceived the included graphs and statistics to be particularly helpful to understand potential risks and benefits of their treatment options. All patients described the provided information as reliable, relevant and tailored to their needs. They found the decision aid easy to understand and balanced (not in favour of NAST or surgery). The amount of the information provided was seen to be just right. Most women received the decision aid after the initial consultation with their surgeon and perceived this as the right delivery timing. Reading and rereading the decision aid at home in between two consultations allowed women to easily integrate the decision aid into their care. They appreciated the opportunity to reconsider their options after consulting their doctor. A number of women reported that their family members used the decision aid as well and thus became more informed and involved in the decision making process. Some women took the decision aid to the next consultation with their doctor to discuss their preferences and concerns further. All patients followed their doctors' treatment recommendation. The decision aid seemed to confirm but not change women's decisions on NAST. Discussion: These initial results suggest that this decision aid is a useful tool to assist breast cancer patients' involvement in the decision about NAST. A quantitative analysis of the decision aid's acceptability, feasibility and efficacy will be reported subsequently.Background: Neoadjuvant systemic therapy (NAST) is a treatment option for selected patients with highly proliferative and/or large operable breast cancer. Whilst survival outcomes are equivalent between up-front surgery and NAST, the decision about treatment sequence can be difficult due to complexity and perceived urgency of the decision. Patients may value the outcomes of these options, such as down staging and prognostication, differently. Involving patients in decisions about their healthcare reduces anxiety, increases quality of life and satisfaction with care. Decision aids can improve patient involvement in health care decisions, but one is not available for the decision about NAST. Aims/Methods: We conducted a prospective, single-arm pre-post study to evaluate a custom-designed decision aid developed for women who have been offered NAST. Eligible patients were: female; aged ≥18 years; diagnosed with an operable invasive breast cancer; considered for NAST with curative intent. Here, we report on the grounded theory qualitative analysis of a convenience sample of 16 semi-structured phone interviews to explore patient experience with this decision aid. Results: Participants' median age was 52 (IQR=41-63), median time since breast cancer diagnosis was 5 months (IQR=2-8). Most were married or living with a partner (81.3%) and had a University level degree (68.8%). Patients perceived the decision aid to be useful for becoming more informed and involved in deciding on NAST. Specifically, the decision aid enhanced patients' understanding of their type of breast cancer and the treatment options available to them by summarising and extending the information they received during the consultation with their doctor. Some women perceived the included graphs and statistics to be particularly helpful to understand potential risks and benefits of their treatment options. All patients described the provided information as reliable, relevant and tailored to their needs. They found the decision aid easy to understand and balanced (not in favour of NAST or surgery). The amount of the information provided was seen to be just right. Most women received the decision aid after the initial consultation with their surgeon and perceived this as the right delivery timing. Reading and rereading the decision aid at home in between two consultations allowed women to easily integrate the decision aid into their care. They appreciated the opportunity to reconsider their options after consulting their doctor. A number of women reported that their family members used the decision aid as well and thus became more informed and involved in the decision making process. Some women took the decision aid to the next consultation with their doctor to discuss their preferences and concerns further. All patients followed their doctors' treatment recommendation. The decision aid seemed to confirm but not change women's decisions on NAST. Discussion: These initial results suggest that this decision aid is a useful tool to assist breast cancer patients' involvement in the decision about NAST. A quantitative analysis of the decision aid's acceptability, feasibility and efficacy will be reported subsequently. Citation Format: Zdenkowski N, Herrmann A, Hall A, Boyle FM, Butow P. Women's experiences with a decision aid for neoadjuvant systemic therapy for operable breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-11-02.

Effects of technology-based contraceptive decision aids: a systematic review and meta-analysis