Conversational assistants are becoming more and more popular, including in healthcare, partly because of the availability and capabilities of Large Language Models. There is a need for controlled, probing evaluations with real stakeholders which can highlight advantages and disadvantages of more traditional architectures and those based on generative AI. We present a within-group user study to compare two versions of a conversational assistant that allows heart failure patients to ask about salt content in food. One version of the system was developed in-house with a neurosymbolic architecture, and one is based on ChatGPT. The evaluation shows that the in-house system is more accurate, completes more tasks and is less verbose than the one based on ChatGPT; on the other hand, the one based on ChatGPT makes fewer speech errors and requires fewer clarifications to complete the task. Patients show no preference for one over the other.

There's an endless number of apps that claim to help with weight loss. But one now claims to be able to tell you exactly how many calories are in your food -- just by taking a picture of what you're eating. Healthify prompts you to take a picture of your meals. Using artificial intelligence (AI), it can recognise food on your plate, as well as how much you have, to generate a nutritional breakdown. A new version of the app, which will be rolled out in the UK in the coming days, includes the feature called Snap 2.0.

Anyone who's eaten out in the past few years has felt and maybe caved to the urge to snap a pic of a particularly well-plated or photogenic entrée for social media. But now, there might finally be a practical and legitimately constructive reason to photograph all those perfectly seared veggies, juicy burgers, or towering desserts with your phone. Former engineers at Google and defense contractor Raytheon have created a cellphone app that can count the calories in any meal by simply taking a photo. Its makers say SnapCalorie -- which is already available for download on Apple's App Store and on Google Play, for free, with a $29.00-per-month premium option -- is better at eyeballing the calorie content of a dish than'professional nutritionists.' 'Human beings are terrible at visually estimating the portion size of a plate of food,' according to SnapCalorie's co-founder, who hopes the app will find wide adoption among dieters too afraid to eat out in case they go over their calorie limit. SnapCalorie's founders say the app is better at eyeballing the calorie content in a dish than'professional nutritionists.' Their AI's secret is a special dataset, Nutrition5k, which the company produced by amassing nutritional data, photos and video of 5000 real-world meals One study compared the industry's leading AI-calorie counting apps to dismal results, finding that SnapCalorie's established rival, Calorie Mama, was only right about 63 percent of the time As of this month, Index Ventures, Y Combinator and even CrossFit CEO Eric Roza himself, have collectively invested a total of $2 million into the team's new start-up. The funders have apparently seen something in the company's co-founders, former Google AI engineer Wade Norris and ex-former Raytheon engineer Scott Baron, as well as their calorie-counting AI, Roboflow.

Higher life expectancies lead to an increased prevalenceof dementia in older adults, which is projected torise dramatically in the future. The link between malnutritionand dementia highlights the need to closelymonitor nutrition as early as possible. However, currentself-report assessment methods are labor-intensive,time-consuming and inaccurate. Technology has the potentialof assisting in nutritional analysis by alleviatingthe cognitive load of recording food intake and lesseningthe burden of care for the elderly. Therefore, we proposean intelligent nutritional assessment system thatwill monitor the dietary patterns of older adults with dementiaat their homes. Our computer vision-based systemconsists of food recognition and portion estimationalgorithms that, together, provide nutritional analysisof an image of a meal. We create a novel food imagedataset on which we achieve an 87.2% recognition accuracy.We apply several well-known segmentation andrecognition algorithms and analyze their suitability tothe food recognition problem.