Despite advancements in healthcare and emerging technologies, falls among older adults remain a significant health issue. Recent research has increasingly focused on developing advanced monitoring methods, predicting, and preventing falls in this population. Achieving high performance in fall prediction requires a clear understanding of relevant features such as gait patterns, balance metrics, muscle strength, and environmental factors. Identifying these key indicators and incorporating data from wearable sensors, medical histories, and demographic information can significantly enhance predictive accuracy. This study proposes an intelligent fall prediction model that anticipates future falls in older adults by continuously monitoring their Activities of Daily Living (ADLs) and detecting abnormalities. The model uses a Deep Belief Network (DBN) that incorporates contrastive divergence for pre-training, backpropagation for fine-tuning, and the Adam Optimizer to minimize loss. Evaluation of the proposed model shows it achieved an accuracy of 91.67%, specificity of 100%, and sensitivity of 90.00% when compared to the Ground Truth (GT) and existing fall prediction approaches. These results suggest that advanced deep learning techniques can effectively assist in early fall risk prediction, potentially reducing the likelihood and severity of falls among older adults.
The aim of this study was to investigate the impact on the delivery, adoption, and effectiveness of Generative Artificial Intelligence integrated wearable devices and internet-of-thing (IoT) for long-term condition monitoring. We adopted preferred reporting items for systematic reviews and meta-analyses review methodology and screened a total of 226 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2020 and 2024. The selection criteria were based on the inclusion of studies that report on the adoption and effectiveness of Generative Artificial Intelligence integrated wearable devices and IoT for long-term condition monitoring. We found wearable health monitoring and personalized patient care plans leveraged Generative Artificial Intelligence (Gen AI) to predict health events by analyzing continuous data from wearables devices and IoT devices like smartwatches, glucose monitors, and various health and well-being sensors. Gen AI models provided tailored advice on physical activity, diet, and sleep, leading to improved health outcomes and user satisfaction. Comparative analysis from reviewed studies demonstrates substantial performance improvements: accuracy enhanced from 85.6% to 97.7%, precision improved from 85.1% to 96.8% and computational latency reduced significantly from 320 ms to 120 ms. Moving AI processing closer to the data source (e.g., on the wearable device itself) can reduce latency and improve real-time decision-making. This is particularly useful for critical health and safety applications. Moreover, robust integration with electronic health records and healthcare providers can enhance the usefulness of data collected by wearables, allowing for more comprehensive and coordinated care. Continued advancements in AI algorithms will improve the predictive capabilities of these systems, enabling even more pro-active and personalized interventions.
Background The integration of large language models (LLMs) like ChatGPT in diagnostic medicine, with a focus on digital pathology, has garnered significant attention. However, understanding the challenges and barriers associated with the use of LLMs in this context is crucial for their successful implementation. Methods A scoping review was conducted to explore the challenges and barriers of using LLMs, in diagnostic medicine with a focus on digital pathology. A comprehensive search was conducted using electronic databases, including PubMed and Google Scholar, for relevant articles published within the past four years. The selected articles were critically analyzed to identify and summarize the challenges and barriers reported in the literature. Results The scoping review identified several challenges and barriers associated with the use of LLMs in diagnostic medicine. These included limitations in contextual understanding and interpretability, biases in training data, ethical considerations, impact on healthcare professionals, and regulatory concerns. Contextual understanding and interpretability challenges arise due to the lack of true understanding of medical concepts and lack of these models being explicitly trained on medical records selected by trained professionals, and the black-box nature of LLMs. Biases in training data pose a risk of perpetuating disparities and inaccuracies in diagnoses. Ethical considerations include patient privacy, data security, and responsible AI use. The integration of LLMs may impact healthcare professionals’ autonomy and decision-making abilities. Regulatory concerns surround the need for guidelines and frameworks to ensure safe and ethical implementation. Conclusion The scoping review highlights the challenges and barriers of using LLMs in diagnostic medicine with a focus on digital pathology. Understanding these challenges is essential for addressing the limitations and developing strategies to overcome barriers. It is critical for health professionals to be involved in the selection of data and fine tuning of the models. Further research, validation, and collaboration between AI developers, healthcare professionals, and regulatory bodies are necessary to ensure the responsible and effective integration of LLMs in diagnostic medicine.
Background The decision to undergo total knee arthroplasty (TKA) is complex, requiring patients to consider the risks and benefits of surgery. It is the clinician’s responsibility to educate patients on their treatment decisions. Patient decision-making aids are a tool for clinicians to utilize with their patients to improve their patients' understanding of their condition and treatment options. With increased emphasis on patient-centered healthcare, implementation of a shared decision-making aid in adult reconstruction clinical practice may help patients make better informed decisions. Purpose Given that the orthopaedic surgery clinic is the primary setting where a patient decides whether to pursue non-operative treatment versus surgery, patients and clinicians in this setting may benefit from the use of a shared decision-making tool (SDMT). The primary goal of this study was to evaluate the utility of a patient-specific SDMT on the patient’s decision regarding knee osteoarthritis (OA) management. Methods A randomized prospective clinical trial was conducted to evaluate the impact of a SDMT regarding treatment for knee OA. Patients were randomized to an intervention arm with the implementation of a patient-specific SDMT or a control arm with a standard of care (SOC) clinic appointment without the use of the tool. The impact of the SDMT was assessed by evaluating patients on Patient-Reported Outcome measures, and their attitudes towards non-operative and operative interventions. Results Both the patient-specific SDMT and control groups demonstrated a statistically significant increase in post-visit understanding of their disease progression. Patients in the SDMT group demonstrated a significant increase in the proportion of patients who would be “extremely likely” to pursue non-operative treatment post-visit. Comparisons between SDMT and control groups demonstrated a significant inclination for the SDMT cohort to enroll in non-operative, conservative management such as corticosteroid or hyaluronic acid injections. Conclusion Use of a patient specific customizable SDMT tool in an adult reconstruction clinic positively impacted patient attitudes toward receiving non-operative treatment for OA and, more importantly, patient understanding of disease post-visit. Trial Registration: This randomized controlled study (ID: NCT05411939, Registered 04/15/2023 at clinicaltrials.gov) was approved by the institutional review board at our university (HIC# 2000032637) and used the CONSORT checklist.
1Cofounder and Chief Medical Officer, Vori Health, Jacksonville, FL, USA M. I. O’Connor, Vori Health, Jacksonville Beach, FL 32250, USA, Email: [email protected] (RE: Rahman A, Jacobson A, Tetreault T, et al. Continuing the Conversation: Letter to the Editor: Equity360: Gender, Race, and Ethnicity: Sex and Fairness in Sports. Clin Orthop Relat Res. 2023. 10.1097/CORR.0000000000002939.) The author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request. The opinions expressed are those of the writer, and do not reflect the opinion or policy of CORR® or The Association of Bone and Joint Surgeons®.
