Safe Integration Research Articles

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

Towards an ontology to capture human attributes in human-robot collaboration

AbstractA core predicate of Industry 5.0 (I5.0) is the integration of human, environmental and social factors with new technologies. The integration of collaborative robots offers increased productivity but raises questions on safety and how robots can respond to varying cognitive and physical attributes. This paper discusses the significance of structured ontologies in managing complex information for proactive, safe, and productive human-robot collaboration. The paper highlights the future work to be undertaken to ensure the safe and fluid integration of humans and robots within I5.0.

Visualization and Visual Analytics in Autonomous Driving.

Autonomous driving is no longer a topic of science fiction. Advancements of autonomous driving technologies are now reliable. Effectively harnessing the information is essential for enhancing the safety, reliability, and efficiency of autonomous vehicles. In this article, we explore the pivotal role of visualization and visual analytics (VA) techniques used in autonomous driving. By employing sophisticated data visualization methods, VA, researchers, and practitioners transform intricate datasets into intuitive visual representations, providing valuable insights for decision-making processes. This article delves into various visualization approaches, including spatial-temporal mapping, interactive dashboards, and machine learning-driven analytics, tailored specifically for autonomous driving scenarios. Furthermore, it investigates the integration of real-time sensor data, sensor coordination with VA, and machine learning algorithms to create comprehensive visualizations. This research advocates for the pivotal role of visualization and VA in shaping the future of autonomous driving systems, fostering innovation, and ensuring the safe integration of self-driving vehicles.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare

Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare

Leveraging Large Language Models for Improved Patient Access and Self-Management: Assessor-Blinded Comparison Between Expert- and AI-Generated Content.

While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access and quality, particularly in specialized domains such as oral health, requires comprehensive evaluation. This study aims to assess the effectiveness of Google Bard, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts. This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts and LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiveness, intent capture, and helpfulness. Additionally, the stability of artificial intelligence responses was also assessed by submitting each question 3 times under consistent conditions. Google Bard excelled in readability but lagged in appropriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P=.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with human experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively), with ChatGPT-4 demonstrating the highest stability and reliability. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses. LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care. The observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings. Future research focuses on developing strategies for the safe integration of LLMs in health care settings.

Comparison of ChatGPT knowledge against 2020 consensus statement on ankyloglossia in children

ObjectiveThis paper evaluates ChatGPT's accuracy and consistency in providing information on ankyloglossia, a congenital oral condition. Assessing alignment with expert consensus, the study explores potential implications for patients relying on AI for medical information. MethodsStatements from the 2020 clinical consensus statement on ankyloglossia were presented to ChatGPT, and its responses were scored using a 9-point Likert scale. The study analyzed the mean and standard deviation of ChatGPT scores for each statement. Statistical analysis was conducted using Excel. ResultsAmong the 63 statements assessed, 67 % of ChatGPT responses closely aligned with expert consensus mean scores. However, 17 % (11/63) were statements in which the ChatGPT mean response was different from the CCS mean by 2.0 or greater, raising concerns about ChatGPT's potential influence in disseminating uncertain or debated medical information. Variations in mean scores highlighted discrepancies, with some statements showing significant deviations from expert opinions. ConclusionWhile ChatGPT mirrored medical viewpoints on ankyloglossia, alignment with non-consensus statements raises caution in relying on it for medical advice. Future research should refine AI models, address inaccuracies, and explore diverse user queries for safe integration into medical decision-making. Despite potential benefits, ongoing examination of ChatGPT's power and limitations is crucial, considering its impact on health equity and information access.

Factors contributing to telemedicine efficacy in pediatric urology

Despite swift implementation of telemedicine with the coronavirus disease 2019 pandemic, there is a paucity of research on its use for management of pediatric urology patients. Specifically, there is limited knowledge and inconsistent data on the effectiveness of telemedicine for various pediatric urologic conditions. Our aim was to evaluate the efficacy of pediatric urological care provided via video visits (VVs) at a large tertiary care children's hospital. We performed a prospective assessment of pediatric urology patients younger than 21 years who had a VV between 5/18/2022 and 5/17/2023. New patients with a testicular diagnosis were not eligible for VVs. After entering the diagnosis and submitting billing using a modifier for telemedicine, clinicians were mandated to select whether the VV allowed for: complete case management (CCM), suboptimal case management (SCM), or incomplete case management (ICM) requiring an in-person visit. Case management categorizations were analyzed according to patient pathology, visit type (i.e., new or established), and patient-centered variables including age, sex, race, insurance type, need for an interpreter, and distress score [a proxy for socioeconomic status]. During the one-year period, there were 3267 telemedicine patients with a median age of 9 years (IQR 3-13) and 57.0% were male. Most VVs (89.3%) were established encounters. Almost 12% of telemedicine patients had external organ pathology (EOP, e.g., phimosis), 43.0% had internal organ pathology (IOP, e.g., hydronephrosis), and 45.1% had functional urological pathology (FUP, e.g., dysfunctional voiding). Clinicians deemed 96.9%, 2.7%, and 0.5% of VVs as having CCM, SCM or ICM, respectively. Telemedicine patients with IOP or FUP were more likely to have CCM, than those with EOP (98.5% and 97.8% vs 87.1%, p<0.0001). On multivariable analysis, patient age, pathology, and visit type were predictive of VV efficacy. Now that telemedicine use has slowed, it is necessary to evaluate and establish its optimal role in pediatric urology. Factors associated with VV efficacy included older patient age, internal organ or functional urological pathology, and established encounters. The long-term success of telemedicine requires suitable patient selection. Telemedicine is quite effective for the management of a wide variety of pediatric urology patients. Continued evaluation of telemedicine, including multi-institutional investigation and corroboration, is necessary for the development of evidence-based best practice guidelines regarding appropriate, safe, and effective integration of telemedicine that drives pediatric urological care forward to meet the demands of the future.

Operational wind and turbulence nowcasting capability for advanced air mobility

The present study introduces “WindAware”, a wind and turbulence prediction system that provides nowcasts of wind and turbulence parameters every 5 min up to 6 h over a predetermined airway over Chicago, Illinois, USA, based on 100 m high-resolution simulations (HRSs). This system is a long short-term memory-based recurrent neural network (LSTM-RNN) that uses existing ground-based wind data to provide nowcasts (forecasts up to 6 h every 5 min) of wind speed, wind direction, wind gust, and eddy dissipation rate to support the Uncrewed Aircraft Systems (UASs) safe integration into the National Airspace System (NAS). These HRSs are validated using both ground-based measurements over airports and upper-air radiosonde observations and their skill is illustrated during lake-breeze events. A reasonable agreement is found between measured and simulated winds especially when the boundary layer is convective, but the timing and inland penetration of lake-breeze events are overall slightly misrepresented. The WindAware model is compared with the classic multilayer perceptron (MLP) and the eXtreme Gradient Boosting (XGBoost) models. It is demonstrated by comparison to high-resolution simulations that WindAware provides more accurate predictions than the MLP over the 6 h lead times and has almost similar performance as the XGBoost model although the XGBoost’s training is the fastest using its parallelized implementation. WindAware also has higher prediction errors when validated against lake-breeze events data due to their under-representation in the training dataset.

Oppositional based artificial rabbits optimization applied for optimal allocation of nonlinear DG in distribution networks considering total harmonic distortion limit

This paper proposes a new optimization approach to identify the optimal placement and sizes of distributed generation (DG) units in a radial distribution network (RDN). Inverter-based DG also known as nonlinear DG (NLDG) units inject nonlinear current into the system, which can cause harmonic distortion. This harmonic distortion can limit the penetration of DG in the system by affecting the stability and reliability of the power system. Therefore, it is essential to consider harmonic distortion when identifying the optimal placement and sizes of DG units in RDNs to ensure DG’s safe and effective integration into the power system. The aim of the proposed work is to minimize real power loss, voltage deviation (VD), and total harmonic distortion limit (THD), and improve the voltage stability index (VSI) by integrating the search strategies of opposition-based learning and artificial rabbits optimization (ARO) to achieve better performance. The proposed algorithm, called opposition-based artificial rabbits optimization (OARO), considers different types of DG units like DG TYPE I and DG TYPE III and is suggested for two familiar RDNs: IEEE 33-bus, and 118-bus systems. The Pareto optimality concept has been introduced to solve the multi-objective problems of the systems. The simulation outcomes have been analyzed by comparing several optimization techniques in various cases. OARO has been shown to produce high-quality results with minimal iterations, reducing the time needed to solve the issue and conserving energy. Overall, the proposed approach offers a promising solution for identifying DG units’ optimal placement and sizes in RDNs while considering various operating scenarios.

Surface coatings of contact lenses for cell sheet transplantation

AbstractThe field of corneal cell sheet engineering has evolved rapidly in the last decade; however, challenges related to transplantation into target tissue still exist, necessitating a new approach to integrate cell sheet technology onto a contact lens surface. In this study, a novel surface‐modified contact lens was developed for corneal regenerative medicine to ensure the easy and safe integration of cell sheets into the target tissue. The lens surface was coated with a thermo‐responsive polymer using the initiated chemical vapor deposition technique, facilitating the separation of tissue‐integrated cell sheets from the coated lens surface by simply lowering the temperature. BEAS‐2B and L929 cells, along with human limbal epithelial cells, were cultured on a crosslinked poly(2‐(dimethylamino)ethyl methacrylate)‐coated lens, providing temperature sensitivity for 14 days. At the end of the incubation period, the cultured cells were harvested as intact monolayer cell sheets through a simple temperature reduction, without enzymatic treatment or additional surgical procedures. Rapid and complete delamination of the cells planted and incubated on the coated lens surface was achieved at room temperature. Therapeutic contact lenses modified with cell sheet technology allowed targeted delivery of cells to the affected area of the cornea.

INTEGRATING IOT IN PEDIATRIC HEALTHCARE: A SYSTEMATIC REVIEW OF CURRENT APPLICATIONS AND FUTURE DIRECTIONS FOR PANCREATIC DISEASES AND OBESITY Tolulope O. Olorunsogo1

In recent years, the integration of Internet of Things (IoT) technologies into healthcare systems has shown immense promise in revolutionizing patient care, particularly in pediatric populations. This systematic review aims to explore the current landscape of IoT applications in pediatric healthcare, specifically focusing on pancreatic diseases and obesity, two significant health challenges affecting children worldwide. The review comprehensively analyzes existing literature, including peer-reviewed articles, conference papers, and relevant reports. By employing systematic search strategies across various databases, studies were identified, screened, and synthesized to elucidate the diverse applications of IoT in managing pancreatic diseases and obesity among pediatric patients. The findings reveal a multitude of IoT applications tailored to pediatric healthcare, ranging from wearable devices for continuous glucose monitoring in diabetic children to smart scales and activity trackers for managing pediatric obesity. These technologies offer real-time monitoring, data analytics, and personalized interventions, enhancing disease management, improving patient outcomes, and promoting patient engagement and adherence to treatment regimens. Furthermore, this review identifies critical challenges and limitations associated with current IoT implementations in pediatric healthcare, such as data privacy concerns, interoperability issues, and the need for robust regulatory frameworks. Addressing these challenges is crucial to maximizing the potential benefits of IoT in pediatric healthcare and ensuring its safe and effective integration into clinical practice. The review outlines promising future directions for IoT in managing pancreatic diseases and obesity in pediatric populations. These include integrating advanced sensors and artificial intelligence algorithms for predictive analytics, developing user-friendly and child-centric IoT solutions, and establishing collaborative networks for data sharing and knowledge exchange. This systematic review underscores the transformative impact of IoT technologies in pediatric healthcare, particularly in managing pancreatic diseases and obesity. By highlighting current applications and future directions, this study provides valuable insights for clinicians, researchers, and policymakers to harness the full potential of IoT in improving pediatric health outcomes.&#x0D; Keywords: IoT, Healthcare, Pancreatic, Disease, Obesity, Pediatric, Review.

Mobile robots for isolation-room hospital settings: A scenario-based preliminary study

Isolated patients pose physical challenges to medical staff owing to the need for protective gear. Additionally, communication issues arise within isolation rooms, hampering patient care. Mobile robots offer potential solutions, allowing for contactless communication and efficient task delegation, thereby reducing the risk of cross-contamination and minimizing staff workload. This preliminary study assessed the usability, acceptability, and potential for improvement of mobile robots in clinical nursing scenarios, focusing on nurses’ perspectives. A preliminary test was conducted using mobile robots in a simulated hospital environment with 30 experienced nurses responsible for isolated patient care. Data were collected through interviews, surveys, and scenario-based tasks. Two scenarios were designed to evaluate the usability and effectiveness of mobile robots in real-world nursing situations. Nurses regarded mobile robots as highly usable and useful in healthcare settings. Robots efficiently handled tasks like remote supply delivery and medication distribution. Nurses recognized the potential for improved communication and efficiency with mobile robots; however, concerns were raised about the robots’ limitations in providing emotional support and potential safety issues during emergencies. This research emphasizes the promising role of mobile robots in enhancing healthcare delivery within isolation rooms. While these findings indicate the potential for mobile robots, careful planning, training, and scenario development are crucial for their safe and effective integration into clinical settings. Further research, tailored scenarios, and a reevaluation of the evolving role of nurses in a technology-augmented healthcare environment are necessary, emphasizing the importance of understanding the capabilities and limitations of robotic assistance in patient care.

Operating speed distributions in off-street cycling facilities by vehicle type and motorization

With the growing popularity of motorized Personal Mobility Devices (PMDs) for urban travel, there is increasing need to understand the impacts on transportation facility operation. Speed is a critical aspect of vehicle performance, and the lack of robust information on PMD operating speeds hinders facility design, regulation, and policy for safe and comfortable PMD integration. The objectives of this study are 1) to derive operating speed distributions for all Personal Mobility Devices in use on off-street “cycling” facilities in metropolitan Vancouver, Canada, and 2) to determine how PMD speeds are influenced by microenvironment factors including facility type, path grade, weather, and path traffic volume. Classified speed data for 27 PMD types were collected in 4 seasons at 12 sampling locations in Vancouver, Canada. Results from the 25,053 observations show that motorization increases speeds by 3, 10, and 13 km/hr for bicycles, skateboards, and scooters, respectively, which tends to homogenize average speeds across PMD types at around 20–24 km/hr. Motorization also decreases the effect of grade on speed by about 2/3rd. Even with motorization, bicycle and other PMD speeds rarely exceed the regulatory limit of 32 km/hr – except for sit-down electric scooters which have exceptionally high speeds. Scenario analysis indicates that even with large penetration rates of motorized PMD, a 30 km/hr design speed for off-street paths would still be appropriate, although less conservative.

“sCT-Feasibility” - a feasibility study for deep learning-based MRI-only brain radiotherapy

BackgroundRadiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.MethodsA total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.DiscussionCompared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow.Trial registrationNCT06106997.

A Bioinspired and Cost-Effective Device for Minimally Invasive Blood Sampling.

Conventional venipuncture is invasive and challenging in low and middle-income countries. Conversely, point-of-care devices paired with fingersticks, although less invasive, suffer from high variability and low blood volume collection. Recently approved microsampling devices address some of these issues but remain cost-prohibitive for resource-limited settings. In this work, a cost-effective microsampling device is described for the collection of liquid blood with minimal invasiveness and sufficient volume retrieval for laboratory analyses or immediate point-of-care testing. Inspired by the anatomy of sanguivorous leeches, the single-use device features a storage compartment for blood collection and a microneedle patch hidden within a suction cup. Finite Element Method simulations, corroborated by mechanical analyses, guide the material selection for device fabrication and design optimization. In piglets, the device successfully collects ≈195µL of blood with minimal invasiveness. Additionally, a tailor-made lid and adapter enable safe fluid transportation and integration with commercially available point-of-care systems for on-site analyses, respectively. Taken together, the proposed platform holds significant promise for enhancing healthcare in the pediatric population by improving patient compliance and reducing the risk of needlestick injuries through concealed microneedles. Most importantly, given its cost-effective fabrication, the open-source microsampling device may have a meaningful impact in resource-limited healthcare settings.

Collision Avoidance Capabilities in High-Density Airspace Using the Universal Access Transceiver ADS-B Messages

The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution for a dependable air-to-air messaging system, but its reliability when stressed with hundreds to thousands of vehicles operating simultaneously is in question. This paper presents an ADS-B model and analyzes the capabilities of the Universal Access Transceiver (UAT), which operates at a frequency of 978 MHz. We use a probabilistic collision avoidance algorithm to examine the impact of varying parameters, including the number of vehicles and the transmission power of the UAT, on the overall safety of the vehicles. Additionally, we investigate the root causes of co-channel interference, proposing enhancements for safe operations in environments with a high density of UAS. Simulation results show message success and collision rates. With our proposed enhancements, UAT ADS-B can provide a decentralized air traffic system that operates safely in high-density situations.

Vietnamese University Students’ Perceptions in Learning English Using ChatGPT

ChatGPT, a generative AI tool, has garnered significant interest due to its advanced capability to perform intricate tasks since it became available to the public. Numerous educators have conducted study on the sophisticated instrument in providing possible advantages for language instruction and acquisition. It is essential to comprehend the attitudes and experiences of students towards developing technologies in order to effectively integrate them into language learning contexts, as technology innovations continue to influence educational landscapes. This study aims to evaluate the attitudes of Vietnamese university students towards utilizing ChatGPT as a means to enhance their English learning. Additionally, it seeks to examine their perspectives of the benefits and drawbacks associated with ChatGPT. The study involved the collection of data from a sample of 100 undergraduate Vietnamese students. The data was obtained by the administration of a questionnaire that included items rated on a 5-point Likert scale. The study’s findings indicate that students perceive ChatGPT as a valuable tool for enhancing their English language learning. However, it is crucial to prioritize the aspect of information security going forward. Collaboration among policymakers, technology experts, academics, and educators is essential to explore the safe and efficient integration of ChatGPT and other developing generative AI tools into English teaching and learning.

Virtual urgent care is here to stay: driving toward safe, equitable, and sustainable integration within emergency medicine.

Virtual care in Canada rapidly expanded during the COVID-19 pandemic in a low-rules environment in response to pressing needs for ongoing access to care amid public health restrictions. Emergency medicine specialists now face the challenge of advising on which virtual urgent care services ought to remain as part of comprehensive emergency care. Consideration must be given to safe, quality, and appropriate care as well as issues of equitable access, public demand, and sustainability (financial and otherwise). The aim of this project was to summarize current literature and expert opinion and formulate recommendations on the path forward for virtual care in emergency medicine. We formed a working group of emergency medicine physicians from across Canada working in a variety of practice settings. The virtual care working group conducted a scoping review of the literature and met monthly to discuss themes and develop recommendations. The final recommendations were circulated to stakeholders for input and subsequently presented at the 2023 Canadian Association of Emergency Physicians (CAEP) Academic Symposium for discussion, feedback, and refinement. The working group developed and reached unanimity on nine recommendations addressing the themes of system design, equity and accessibility, quality and patient safety, education and curriculum, financial models, and sustainability of virtual urgent care services in Canada. Virtual urgent care has become an established service in the Canadian health care system. Emergency medicine specialists are uniquely suited to provide leadership and guidance on the optimal delivery of these services to enhance and complement emergency care in Canada.

Harnessing the open access version of ChatGPT for enhanced clinical opinions.

With the advent of Large Language Models (LLMs) like ChatGPT, the integration of Generative Artificial Intelligence (GAI) into clinical medicine is becoming increasingly feasible. This study aimed to evaluate the ability of the freely available ChatGPT-3.5 to generate complex differential diagnoses, comparing its output to case records of the Massachusetts General Hospital published in the New England Journal of Medicine (NEJM). Forty case records were presented to ChatGPT-3.5, prompting it to provide a differential diagnosis and then narrow it down to the most likely diagnosis. The results indicated that the final diagnosis was included in ChatGPT-3.5's original differential list in 42.5% of the cases. After narrowing, ChatGPT correctly determined the final diagnosis in 27.5% of the cases, demonstrating a decrease in accuracy compared to previous studies using common chief complaints. These findings emphasize the necessity for further investigation into the capabilities and limitations of LLMs in clinical scenarios while highlighting the potential role of GAI as an augmented clinical opinion. Anticipating the growth and enhancement of GAI tools like ChatGPT, physicians and other healthcare workers will likely find increasing support in generating differential diagnoses. However, continued exploration and regulation are essential to ensure the safe and effective integration of GAI into healthcare practice. Future studies may seek to compare newer versions of ChatGPT or investigate patient outcomes with physicians integrating this GAI technology. Understanding and expanding GAI's capabilities, particularly in differential diagnosis, may foster innovation and provide additional resources, especially in underserved areas in the medical field.