University of California, Irvine

A group of scholars calculated the amount of energy used by AI tools for the tasks of writing and illustrating and compared it to the average amount of energy humans use for the same processes. Their results showed artificial intelligence results in hundreds of times less carbon emissions than humans. This does not mean, however, that AI can or should replace humans in those tasks, simply that its energy usage is less. The better approach is a partnership between humans and AI, the authors write. With the evolution of artificial intelligence comes discussion of the technology's environmental impact. A new study has found that for the tasks of writing and illustrating, AI emits hundreds of times less carbon than humans performing the same tasks. That does not mean, however, that AI can or should replace human writers and illustrators, the study's authors argue. Andrew Torrance, Paul E. Wilson Distinguished Professor of Law at KU, is co-author of a study that compared established systems such as ChatGPT, Bloom AI, DALL-E2 and others completing writing and illustrating to that of humans. Like cryptocurrency, AI has been subject to debate about the amount of energy it uses and its contributions to climate change. Human emissions and environmental impact have long been studied, but comparisons between the two have been scant. The authors conducted a comparison and found that AI systems emit between 130 and 1,500 times less CO2e (carbon dioxide equivalent) per page of text generated than human writers and illustration systems between 310 and 2,900 times less CO2e per image than humans. "I like to think of myself as driven by data, not just what I feel is true. We've had discussions about something that appears to be true in terms of AI emissions, but we wanted to look at the data and see if it truly is more efficient," Torrance said. "When we did it, the results were kind of astonishing. Even by conservative estimates, AI is extremely less wasteful." The study, co-written with Bill Tomlinson, Rebecca Black and Donald Patterson of the University of California-Irvine, was published in the journal Nature. To calculate the carbon footprint of a person writing, the researchers consulted the Energy Budget, a measure that considers the amount of energy used in certain tasks for a set period of time. For example, it is well established how much energy a computer with word processing software uses per hour. When multiplied by the average time it takes a person to write a page of text, on average, 250 words, an estimate can be arrived at. Using the same amount of energy used by the CPUs that operate AI such as ChatGPT, which can produce text much faster, produces an estimate for AI. Researchers also considered per capita emissions of individuals in the United States and India. Residents of the former have approximate annual emissions of 15 metric tons CO2e per year, while the latter is an average of 1.9 metric tons. The two nations were chosen as they have the highest and lowest respective per capita environmental impact of countries with population higher than 300 million, and to provide a look at different levels of emissions in different parts of the world in comparison to AI. Results showed that Bloom is 1,400 times less impactful than a U.S. resident writing a page of text and 180 times less impactful than a resident of India. In terms of illustration, results showed that DALL-E2 emits approximately 2,500 times less CO2e than a human artist and 310 times less than an India-based artist. Figures for Midjourney were 2,900 times less for the former and 370 times less for the latter. As technologies improve and societies evolve, those figures are almost certain to change as well, Torrance said. The authors wrote that carbon emissions are only one factor to consider when comparing AI production to human output. As the technologies exist now, they are often not capable of producing the quality of writing or art that a human can. As they improve, they hold the potential to both eliminate existing jobs and create new ones. Loss of employment has potential for substantial economic, societal and other forms of destabilization. For those and other reasons, the authors wrote, the best path forward is likely a collaboration between AI and human efforts, or a system in which people can use AI to be more efficient in their work and retain control of final products. Legal issues such as the use of copyrighted material in training sets for AI must be considered, the authors wrote, as does the potential for an increase in artificially produced material to result in an increase in the energy it uses and resulting emissions. Collaboration between the two is the most beneficial use of both AI and human labor, the authors wrote. "We don't say AI is inherently good or that it is empirically better, just that when we looked at it in these instances, it was less energy consumptive," Torrance said. The research was conducted to improve understanding of AI and its environmental impact and to address the United Nation Sustainable Development Goals of ensuring sustainable consumption and production patterns and taking urgent action to combat climate change and its impacts, the researchers wrote. For their part, the authors have begun to use AI as an aid in producing drafts for some of their writing, but they also agree on the necessity of carefully editing, and adding to, such drafts manually. "This is not a curse, it's a boon," Torrance said of AI. "I think this will help make good writers great, mediocre writers good and democratize writing. It can make people more productive and can be an empowerment of human potential. I'm hugely optimistic that technology is getting better in most respects and lightening the effects we have on the Earth. We hope this is just the beginning and that people continue to dig into this issue further."

At the University of California Irvine Sue & Bill Gross School of Nursing, faculty members are developing ways for artificial intelligence to help deliver better patient care and improved outcomes. Amir Rahmani is a professor at the nursing school – and also a computer science professor at the Donald Bren School of Information and Computer Sciences, a professor of electrical engineering at the Henry Samueli School of Engineering and an associate director of the Institute for Future Health . His latest project is openCHA, a conversational health agent with a personalized large language model-powered framework. He's developing it in collaboration with Mahyar Abbasian, Iman Azimi and Ramesh Jain, all from UCI's School of Information and Computer Sciences. We interviewed Rahmani to learn more about openCHA – discussing LLMs' need for more comprehensive capabilities; how developers will be able to integrate external data sources, knowledge bases and analysis models into their systems using openCHA; and next steps getting openCHA out onto the healthcare market. Q. In general terms, please describe openCHA and your goals for the technology. A. In the realm of healthcare, the abundance of misinformation can leave individuals feeling lost and uncertain. Sorting through the sea of conflicting information online is no easy feat, and without proper guidance, it's easy to fall prey to inaccurate advice. Language barriers further compound this issue, making it difficult for individuals to access the help they need. But amidst this confusion lies a potential solution: personalized health data. By leveraging biomarkers, genomics , images and other personal information, individuals can gain clearer insights into their health and make more informed decisions. This is where recent advancements in AI come into play. With the ability to analyze vast amounts of data and provide personalized recommendations, AI is becoming an invaluable tool in navigating the complex landscape of healthcare. However, accessibility to these AI-driven solutions remains a challenge, akin to searching for a needle in a haystack. Enter the large language model era, which is poised to revolutionize how we access and interact with healthcare information, offering a beacon of hope in an otherwise murky sea of misinformation. In recent years, LLM-based conversational systems have been shaking things up big time. These systems are like the cool kids on the block, giving us access to loads of text info and serving up conversations that actually make sense. But here's the thing: When it comes to managing health, we need more than just your run-of-the-mill LLM. We're talking about conversational health agents (CHAs) – the superheroes of the health world. These guys need to be able to talk the talk, adapting to your ever-changing health needs and analyzing your personal data like a pro. And guess what? They're powered by those trusty LLMs, making sure they understand you and can give you the personalized support you need, whether it's answering your burning health questions or just lending an empathetic ear. Now, let's talk about openCHA. Right now, we're at the brink of creating frameworks that can dish out info in the friendliest, most culturally sensitive way possible. That's where openCHA comes in – it's like the toolkit for developers looking to build CHAs. Our goal? To make sure CHAs can really connect with users, giving them personalized, caring responses to their health questions. With openCHA, we're talking about enabling the integration of all sorts of data sources, knowledge bases and analytical models to totally revamp how CHAs interact with people. This framework is a game-changer, arming CHAs with the brains and resources they need to give spot-on health advice that's tailored just for you. Say hello to a whole new level of health companionship – openCHA is here to make sure you get the info you need, when you need it. Q. You say LLMs need more comprehensive capabilities, including critical thinking, knowledge acquisition and problem-solving abilities. How are you accounting for this in openCHA's LLM-powered framework? A. Let me introduce you to the orchestrator, the cornerstone of our framework, designed to emulate human behavior within the healthcare process. At its core, this orchestrator comprises two LLMs and one executor. One LLM serves as the planner, coordinating with the executor to gather essential information and conduct necessary analyses. Leveraging well-established prompting techniques, this primary LLM navigates the planning and problem-solving process, providing transparent reasoning behind its responses and decisions. Within the openCHA framework, this capability allows for the decomposition of user queries into manageable subproblems, facilitating the execution of tasks required to gather pertinent information. Once all relevant data is collected, the second LLM takes charge, utilizing the amassed information to furnish users with reliable answers. This structured approach has proved a comprehensive and dependable response to user inquiries, fostering confidence and trust in the openCHA system. Q. How will developers be able to integrate external data sources, knowledge bases and analysis models into their systems using openCHA? A. We've rolled out an open-source codebase, offering developers all the tools they need to seamlessly integrate existing datasets, knowledge bases and analysis models to CHAs. We've kept the code flexible and modular, making it a breeze to add new external sources with just a few lines of codes. The orchestrator handles the heavy lifting, managing the logic effortlessly, so developers can focus on what they do best. Q. What are the next steps for you in getting openCHA out into the healthcare market? A. Currently, openCHA is an open-source solution to build community. But with the vision of enabler tech for making applications happen. Prototyping. It can be vague. Our mission is to foster a thriving community centered around openCHA, sparking innovation within the realm of CHAs. Our focus is on establishing an open architecture for openCHA, forging connections with other open health technologies, accessing open-content resources, and shaping future standards for CHAs. We're passionate about raising awareness of the future of healthcare, highlighting the pivotal role of LLMs . By integrating execution and planning methodologies, our goal is to deliver top-notch health solutions that meet the ever-changing needs of users. Our ultimate vision is to cultivate a collaborative environment where stakeholders can freely exchange ideas, share expertise and collectively drive the conversational health technology field forward. Through collaborative efforts and shared insights, we're dedicated to guiding the development of CHAs toward increased effectiveness and relevance in addressing healthcare challenges. Follow Bill's HIT coverage on LinkedIn: Bill Siwicki Email him: bsiwicki@himss.org Healthcare IT News is a HIMSS Media publication.

LEXINGTON, Mass.--(BUSINESS WIRE)-- Aldeyra Therapeutics Inc. (Nasdaq: ALDX) (Aldeyra), a biotechnology company devoted to discovering and developing innovative therapies designed to treat immune-mediated and metabolic diseases, today announced the clinical development plan intended to enable resubmission of a New Drug Application (NDA) of topical ocular 0.25% reproxalap, an investigational RASP modulator, for the treatment of dry eye disease to the U.S. Food and Drug Administration (FDA). Following discussions with the FDA, Aldeyra intends to initiate a dry eye chamber clinical trial in the first half of 2024. Contingent on positive results from the planned clinical trial, NDA resubmission is expected in the second half of 2024. Based on FDA guidance, the planned review period for the potential NDA resubmission is expected to be six months. “The planned dry eye chamber clinical trial, if successful, could represent the first pivotal demonstration of rapid improvement in the symptoms of dry eye disease, a potentially debilitating and increasingly pervasive condition that affects millions of patients worldwide,” stated Todd C. Brady, M.D., Ph.D., President and Chief Executive Officer of Aldeyra. In the proposed dry eye chamber trial, patients will be administered vehicle (the drug product without the active ingredient) before and during exposure to a dry eye chamber. Qualifying patients will subsequently be randomized to receive either reproxalap or vehicle before and during exposure to an additional dry eye chamber. Approximately 100 patients are expected to be enrolled to assess the primary endpoint of ocular discomfort. The design and statistical powering for the proposed trial are derived from four previously completed dry eye chamber clinical trials with reproxalap. Based on the data from the previous clinical trials, when analyzed with the planned trial design and statistical plan, ocular discomfort in the dry eye chamber following treatment with reproxalap was statistically lower than that of vehicle (p=0.0003). The planned clinical trial is expected to be more than 90% powered to detect a difference between treatment groups. “Affecting hundreds of millions of people globally, dry eye disease is a multifactorial progressive disease that impacts quality of life,” stated Sumit Garg, M.D., Professor of Ophthalmology at University of California Irvine. “We are excited about the novel upstream mechanism of action of RASP modulation and the potential to provide rapid relief of signs and symptoms of dry eye.” A clinical trial at a different dry eye chamber, in addition to a traditional six-week field clinical trial, are expected to be conducted in parallel with the planned clinical trial as part of a comprehensive strategy designed to account for disease heterogeneity and potential differences in clinical sites and environment. Consistent with previously disclosed guidance and based on the current operating plan, cash and cash equivalents of $142.8 million as of December 31, 2023 are projected to be sufficient to fund operations beyond 2026. Conference Call & Webcast Information Aldeyra will host a conference call at 8:00 a.m. ET today, March 28, 2024, to discuss the clinical development plan for resubmission of the new drug application for reproxalap in dry eye disease. The dial-in numbers are (833) 470-1428 for domestic callers and (404) 975-4839 for international callers. The access code is 515106. A live webcast of the conference call will be available on the Investor Relations page of the company’s website at . After the live webcast, the event will remain archived on the Aldeyra Therapeutics website for 90 days. About Aldeyra Aldeyra Therapeutics is a biotechnology company devoted to discovering innovative therapies designed to treat immune-mediated and metabolic diseases. Our approach is to develop pharmaceuticals that modulate protein systems, instead of directly inhibiting or activating single protein targets, with the goal of optimizing multiple pathways at once while minimizing toxicity. Our product candidates include RASP (reactive aldehyde species) modulators ADX‑629, ADX‑246, ADX‑248, and chemically related molecules for the potential treatment of systemic and retinal immune-mediated and metabolic diseases. Our late-stage product candidates are reproxalap, a RASP modulator for the potential treatment of dry eye disease and allergic conjunctivitis, and ADX-2191, a novel formulation of intravitreal methotrexate for the potential treatment of retinitis pigmentosa. About Reproxalap Reproxalap is an investigational new drug candidate in development for the treatment of dry eye disease and allergic conjunctivitis, two of the largest markets in ophthalmology. Reproxalap is a first-in-class small-molecule modulator of RASP, which are elevated in ocular and systemic inflammatory diseases. The mechanism of action of reproxalap has been supported by the demonstration of statistically significant and clinically relevant activity in multiple physiologically distinct late-phase clinical indications. Reproxalap has been studied in more than 2,400 patients with no observed safety concerns; mild and transient instillation site irritation is the most commonly reported adverse event in clinical trials. Safe Harbor Statement This release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, including, but not limited to, statements regarding Aldeyra’s future expectations, plans, and prospects, including without limitation statements regarding: the goals, opportunity, and potential for reproxalap; the outcome and expected timing and the results of Aldeyra’s planned clinical trials; the outcome and timing of the FDA’s review, acceptance and/or approval of a NDA resubmission for reproxalap and the adequacy of the data included in the original NDA and the potential NDA resubmission; and Aldeyra’s projected cash runway. Aldeyra intends such forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 21E of the Securities Exchange Act of 1934 and the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by terms such as, but not limited to, “may,” “might,” “will,” “objective,” “intend,” “should,” "could," “can,” “would,” “expect,” “believe,” “anticipate,” “project,” “on track,” “scheduled,” “target,” “design,” “estimate,” “predict,” “contemplates,” “likely,” “potential,” “continue,” “ongoing,” “aim,” “plan,” or the negative of these terms, and similar expressions intended to identify forward-looking statements. Such forward-looking statements are based upon current expectations that involve risks, changes in circumstances, assumptions, and uncertainties. Aldeyra is at an early stage of development and may not ever have any products that generate significant revenue. All of Aldeyra's development timelines may be subject to adjustment depending on recruitment rate, regulatory review, preclinical and clinical results, funding, and other factors that could delay the initiation, enrollment, or completion of clinical trials. Important factors that could cause actual results to differ materially from those reflected in Aldeyra's forward-looking statements include, among others, the timing of enrollment, commencement and completion of Aldeyra's clinical trials, the timing and success of preclinical studies and clinical trials conducted by Aldeyra and its development partners; delay in or failure to obtain regulatory approval of Aldeyra's product candidates, including as a result of the FDA not accepting Aldeyra’s regulatory filings, issuing a complete response letter, or requiring additional clinical trials or data prior to review or approval of such filings or in connection with resubmissions of such filings; the ability to maintain regulatory approval of Aldeyra's product candidates, and the labeling for any approved products; the risk that prior results, such as signals of safety, activity, or durability of effect, observed from preclinical or clinical trials, will not be replicated or will not continue in ongoing or future studies or clinical trials involving Aldeyra's product candidates in clinical trials focused on the same or different indications; the scope, progress, expansion, and costs of developing and commercializing Aldeyra's product candidates; uncertainty as to Aldeyra’s ability to commercialize (alone or with others) and obtain reimbursement for Aldeyra's product candidates following regulatory approval, if any; the size and growth of the potential markets and pricing for Aldeyra's product candidates and the ability to serve those markets; Aldeyra's expectations regarding Aldeyra's expenses and future revenue, the timing of future revenue, the sufficiency or use of Aldeyra's cash resources and needs for additional financing; the rate and degree of market acceptance of any of Aldeyra's product candidates; Aldeyra's expectations regarding competition; Aldeyra's anticipated growth strategies; Aldeyra's ability to attract or retain key personnel; Aldeyra’s commercialization, marketing and manufacturing capabilities and strategy; Aldeyra's ability to establish and maintain development partnerships; Aldeyra’s ability to successfully integrate acquisitions into its business; Aldeyra's expectations regarding federal, state, and foreign regulatory requirements; political, economic, legal, social, and health risks, public health measures, and war or other military actions, that may affect Aldeyra’s business or the global economy; regulatory developments in the United States and foreign countries; Aldeyra's ability to obtain and maintain intellectual property protection for its product candidates; the anticipated trends and challenges in Aldeyra's business and the market in which it operates; and other factors that are described in the “Risk Factors” and “Management's Discussion and Analysis of Financial Condition and Results of Operations” sections of Aldeyra's Annual Report on Form 10-K for the year ended December 31, 2023, which is on the Securities and Exchange Commission (SEC) and available on the SEC's website at . Additional factors may be described in those sections of Aldeyra’s Quarterly Report on Form 10-Q for the quarter ended March 31, 2024, expected to be filed with the SEC in the second quarter of 2024, and Aldeyra’s other filings with the SEC. In addition to the risks described above and in Aldeyra's other filings with the SEC, other unknown or unpredictable factors also could affect Aldeyra's results. No forward-looking statements can be guaranteed and actual results may differ materially from such statements. The information in this release is provided only as of the date of this release, and Aldeyra undertakes no obligation to update any forward-looking statements contained in this release on account of new information, future events, or otherwise, except as required by law.