Ethical data acquisition for LLMs and AI algorithms in healthcare.

Artificial intelligence (AI) algorithms will become increasingly integrated into our healthcare systems in the coming decades. These algorithms require large volumes of data for development and fine-tuning. Patient data is typically acquired for AI algorithms through an opt-out system in the United States, while others support an opt-in model.

Minimally Invasive vs Open First Ray Dorsiflexion Osteotomy: Radiographic Outcomes and Early Complications Report.

Background: There is limited literature comparing open and minimally invasive surgical (MIS) techniques for first ray dorsiflexion osteotomy (DFO). This study is the first of its kind to report early healing and complication rates of patients undergoing MIS vs open first ray DFO.

Methods: A retrospective cohort review of 28 patients who underwent a first ray DFO procedure at an academic medical center between 2015 and 2024 was conducted.

Generative AI and large language models in health care: pathways to implementation.

Generative AI is designed to create new content from trained parameters. Learning from large amounts of data, many of these models aim to simulate human conversation. Generative AI is being applied to many different sectors.

AI-based diabetes care: risk prediction models and implementation concerns.

The utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al.

Deep learning models across the range of skin disease.

We explore the evolving landscape of diagnostic artificial intelligence (AI) in dermatology, particularly focusing on deep learning models for a wide array of skin diseases beyond skin cancer. We critically analyze the current state of AI in dermatology, its potential in enhancing diagnostic accuracy, and the challenges it faces in terms of bias, applicability, and therapeutic recommendations.

AI-based skin cancer detection: the balance between access and overutilization.

Gregoor et al. evaluated the healthcare implications and costs of an AI-enabled mobile health app for skin cancer detection, involving 18,960 beneficiaries of a Netherlands insurer. They report a 32% increase in claims for premalignant and malignant skin lesions among app users, largely attributed to benign skin lesions and leading to higher annual costs for app users (€64.

Disparities in cannabis use and documentation in electronic health records among children and young adults.

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis's effects on health outcomes.

Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches.

AI-based prediction models demonstrate equal or surpassing performance compared to experienced physicians in various research settings. However, only a few have made it into clinical practice. Further, there is no standardized protocol for integrating AI-based physician support systems into the daily clinical routine to improve healthcare delivery.

Bias in AI-based models for medical applications: challenges and mitigation strategies.

Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI applications hold promise as tools to predict surgical outcomes, assess technical skills, or guide surgeons intraoperatively via computer vision. On the other hand, AI systems can also suffer from bias, compounding existing inequities in socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation.

The digital transformation of surgery.

Rapid advances in digital technology and artificial intelligence in recent years have already begun to transform many industries, and are beginning to make headway into healthcare. There is tremendous potential for new digital technologies to improve the care of surgical patients. In this piece, we highlight work being done to advance surgical care using machine learning, computer vision, wearable devices, remote patient monitoring, and virtual and augmented reality.

Automating the overburdened clinical coding system: challenges and next steps.

Artificial intelligence (AI) and natural language processing (NLP) have found a highly promising application in automated clinical coding (ACC), an innovation that will have profound impacts on the clinical coding industry, billing and revenue management, and potentially clinical care itself. Dong et al. recently analyzed the technical challenges of ACC and proposed future directions.

Promoting racial equity in digital health: applying a cross-disciplinary equity framework.

Even as innovation occurs within digital medicine, challenges around equity and racial health disparities remain. Golden et al. evaluate structural racism in their recent paper focused on reproductive health.

Has increased telehealth access during COVID-19 led to over-utilization of primary care?

Telehealth use for primary care has skyrocketed since the onset of the COVID-19 pandemic. Enthusiasts have praised this new medium of delivery as a way to increase access to care while potentially reducing spending. Over two years into the pandemic, the question of whether telehealth will lead to an increase in primary care utilization and spending has been met with contradictory answers.

Defining digital surgery for the future.

Innovations in robotics, virtual and augmented reality, and artificial intelligence are being rapidly adopted as tools of "digital surgery". Despite its quickly emerging role, digital surgery is not well understood. A recent study defines the term itself, and then specifies ethical issues specific to the field.

Intelligent risk prediction in public health using wearable device data.

The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings.

Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.

Health digital twins are defined as virtual representations ("digital twin") of patients ("physical twin") that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables. With appropriate use, HDTs can model random perturbations on the digital twin to gain insight into the expected behavior of the physical twin-offering groundbreaking applications in precision medicine, clinical trials, and public health. Main considerations for translating HDT research into clinical practice include computational requirements, clinical implementation, as well as data governance, and product oversight.

Leveraging reimbursement strategies to guide value-based adoption and utilization of medical AI.

With the increasing number of FDA-approved artificial intelligence (AI) systems, the financing of these technologies has become a primary gatekeeper to mass clinical adoption. Reimbursement models adapted for current payment schemes, including per-use rates, are feasible for early AI products. Alternative and complementary models may offer future payment options for value-based AI.

Watching Parkinson's disease with wrist-based sensors.

Parkinson's disease (PD) lacks sensitive, objective, and reliable measures for disease progression and response. This presents a challenge for clinical trials given the multifaceted and fluctuating nature of PD symptoms. Innovations in digital health and wearable sensors promise to more precisely measure aspects of patient function and well-being.

Multinational landscape of health app policy: toward regulatory consensus on digital health.

Due to its enormous capacity for benefit, harm, and cost, health care is among the most tightly regulated industries in the world. But with the rise of smartphones, an explosion of direct-to-consumer mobile health applications has challenged the role of centralized gatekeepers. As interest in health apps continue to climb, national regulatory bodies have turned their attention toward strategies to protect consumers from apps that mine and sell health data, recommend unsafe practices, or simply do not work as advertised.

Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico.

As clinicians and scientists gather more data on the clinical trajectory of COVID-19 and the biology of its causative agent, the SARS-CoV-2 virus, novel strategies are needed to integrate these data to inform new therapies. A recent study by Howell et al. introduces a network model of viral-host interactions to produce explainable and testable predictions for treatment effects.

An improved fluorescent noncanonical amino acid for measuring conformational distributions using time-resolved transition metal ion FRET.

With the recent explosion in high-resolution protein structures, one of the next frontiers in biology is elucidating the mechanisms by which conformational rearrangements in proteins are regulated to meet the needs of cells under changing conditions. Rigorously measuring protein energetics and dynamics requires the development of new methods that can resolve structural heterogeneity and conformational distributions. We have previously developed steady-state transition metal ion fluorescence resonance energy transfer (tmFRET) approaches using a fluorescent noncanonical amino acid donor (Anap) and transition metal ion acceptor to probe conformational rearrangements in soluble and membrane proteins.

Chronic Breast Pain Prior to Breast Cancer Surgery Is Associated with Worse Acute Postoperative Pain Outcomes.

Acute postoperative pain is associated with adverse short and long-term outcomes among women undergoing surgery for breast cancer. Previous studies identified preexisting pain as a predictor of postoperative pain, but rarely accounted for pain location or chronicity. This study leveraged a multinational pain registry, PAIN OUT, to: (1) characterize patient subgroups based on preexisting chronic breast pain status and (2) determine the association of preexisting chronic pain with acute postoperative pain-related patient-reported outcomes and opioid consumption following breast cancer surgery.