Radiology report evaluation is a crucial part of radiologists' training and plays a key role in ensuring diagnostic accuracy. As part of the standard reporting workflow, a junior radiologist typically prepares a preliminary report, which is then reviewed and edited by a senior radiologist to produce the final report. Identifying semantic differences between preliminary and final reports is essential for junior doctors, both as a training tool and to help uncover gaps in clinical knowledge. While AI in radiology is a rapidly growing field, the application of large language models (LLMs) remains challenging due to the need for specialised domain knowledge. In this paper, we explore the ability of LLMs to provide explainable and accurate comparisons of reports in the radiology domain. We begin by comparing the performance of several LLMs in comparing radiology reports. We then assess a more traditional approach based on Named-Entity-Recognition (NER). However, both approaches exhibit limitations in delivering accurate feedback on semantic similarity. To address this, we propose Llama-EntScore, a semantic similarity scoring method using a combination of Llama 3.1 and NER with tunable weights to emphasise or de-emphasise specific types of differences. Our approach generates a quantitative similarity score for tracking progress and also gives an interpretation of the score that aims to offer valuable guidance in reviewing and refining their reporting. We find our method achieves 67% exact-match accuracy and 93% accuracy within +/- 1 when compared to radiologist-provided ground truth scores - outperforming both LLMs and NER used independently. Code is available at: https://github.com/otmive/llama_reports

We propose leveraging chromostereopsis, Building upon traditional methods that a perceptual phenomenon inducing depth utilize color-coding and textual formatting, perception through color contrast, as a our approach employs a dark terminal novel approach to visually differentiating background to enhance the optical illusion conversational roles in text-based AI interfaces.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. The National Transportation Safety Board released its preliminary report on Thursday into what went wrong at a Florida drone show last month that caused some of the aircraft to go rogue, leaving a little boy seriously injured. The mishap took place during a Christmas light show put on by Sky Elements at Lake Eola Park in Orlando on Dec. 21, 2024. Hundreds of people were watching the aerial show when several of the drones flew out of formation – some colliding with one another before falling to the ground. One of the rogue drones struck a 7-year-old boy in the face and chest, knocking him out upon impact.

Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast imaging, aiding in the early detection and characterization of breast abnormalities. Interpreting breast ultrasound images commonly involves creating comprehensive medical reports, containing vital information to promptly assess the patient's condition. However, the ultrasound imaging system necessitates capturing multiple images of various parts to compile a single report, presenting a time-consuming challenge. To address this problem, we propose the integration of multiple image analysis tools through a LangChain using Large Language Models (LLM), into the breast reporting process. Through a combination of designated tools and text generation through LangChain, our method can accurately extract relevant features from ultrasound images, interpret them in a clinical context, and produce comprehensive and standardized reports. This approach not only reduces the burden on radiologists and healthcare professionals but also enhances the consistency and quality of reports. The extensive experiments shows that each tools involved in the proposed method can offer qualitatively and quantitatively significant results. Furthermore, clinical evaluation on the generated reports demonstrates that the proposed method can make report in clinically meaningful way.

Tesla reported its worst-ever quarterly loss and said its Model 3 production target remains on track. In this Friday March 23, 2018 photo provided by KTVU, emergency personnel work a the scene where a Tesla electric SUV crashed into a barrier on U.S. Highway 101 in Mountain View, Calif. The National Transportation Safety Board has sent two investigators to look into a fatal crash and fire Friday in California that involved a Tesla electric SUV. The agency says on Twitter that it's not clear whether the Tesla Model X was operating on its semi-autonomous control system called Autopilot at the time. Investigators will study the fire that broke out after the crash.

One of the greatest ironies in this still-nascent era of self-driving cars is that humans are the backup safety drivers for these autonomous systems, while the systems themselves are supposed to replace human drivers and all our follies. Earlier this week, a preliminary report from the NTSB indicated that the Uber self-driving car that killed a woman in Arizona earlier this year, did in fact "see" the woman in the seconds before the crash occurred. Transportation writer Aarian Marshall and editor Alex Davies join the Gadget Lab podcast this week to discuss the issues that surround "software that's not yet ready to replace humans, and humans that are ill-equipped to keep their would-be replacements from doing harm." And of course, we couldn't have a conversation about the future of transportation without talking about Elon Musk. Also, Alex writes about the follies of humans act as backup safety drivers, while Aarian lays out California's heavy-handed plans to regulate autonomous vehicles.

An autonomous Uber car spotted a pedestrian about six seconds before fatally hitting her but did not stop because the system used to automatically apply brakes in potentially dangerous situations had been disabled, US federal investigators said. In a preliminary report on the crash, the National Transportation Safety Board (NTSB) said emergency braking maneuvers are not enabled while Uber's cars are under computer control'to reduce the potential for erratic vehicle behavior'. Instead, Uber relies on a human backup driver to intervene but the system is not designed to alert the driver. Investigators examine a driverless Uber SUV that fatally struck a woman in Arizona. The National Transportation Safety Board (NTSB) said emergency braking maneuvers are not enabled while Uber's cars are under computer control In the crash in March, the driver Rafaela Vasquez began steering less than a second before impact but did not brake until less than a second after impact, according to the preliminary report, which does not determine fault.

Raw video: Cameras mounted inside the car catches the fatal moment. Authorites are investigating the cause of the crash. The self-driving Uber SUV that struck and killed Elaine Herzberg in Tempe, Ariz., in March picked her up on its sensors six seconds before it hit her, but did not determine that it needed to stop or evade her until it was too late, according to federal investigators. Herzberg was jaywalking her bicycle across a four-lane section of road on the night of March 18 when the Volvo XC90 SUV ran into her. A preliminary report on the accident from the National Transportation Safety Board issued on Thursday said that a review of the data from the car shows that it first identified her as an unknown object, then as a vehicle and finally as a bicycle.

An autonomous Uber car spotted a pedestrian about six seconds before fatally hitting her but did not stop because the system used to automatically apply brakes in potentially dangerous situations had been disabled, US federal investigators said. In a preliminary report on the crash, the National Transportation Safety Board (NTSB) said emergency braking manoeuvres are not enabled while Uber's cars are under computer control "to reduce the potential for erratic vehicle behaviour". Instead, Uber relies on a human backup driver to intervene but the system is not designed to alert the driver. In the crash in March, the driver began steering less than a second before impact but did not brake until less than a second after impact, according to the preliminary report, which does not determine fault. A video of the crash showed the driver looking down just before the vehicle struck and killed 49-year-old Elaine Herzberg in Tempe, Arizona.

An autonomous Uber vehicle's emergency braking system was switched off in the critical seconds before the car fatally struck a pedestrian in Arizona earlier this year, a report found. The lethal March crash was the first known time a self-driving vehicle killed a pedestrian, and it raised grave questions about the future of the burgeoning autonomous vehicle industry. In the aftermath, Uber has suspended its self-driving car initiative in Arizona. In a preliminary report detailing how the accident occurred, the National Transportation Safety Board (NTSB) noted that the car was equipped with an automatic emergency braking that does not function when the car is being controlled by its computer system, relying instead on a human to take over. Self-driving cars like the one involved in the crash have an autonomous mode, in which a computer does the driving, and a manual mode in which a human backup driver takes over.