{"id":37670,"date":"2024-03-26T15:22:28","date_gmt":"2024-03-26T19:22:28","guid":{"rendered":"https:\/\/globalwellnessinstitute.org\/?p=37670"},"modified":"2024-12-10T13:58:14","modified_gmt":"2024-12-10T18:58:14","slug":"2024-ai-initiative-trends-2","status":"publish","type":"post","link":"https:\/\/globalwellnessinstitute.org\/global-wellness-institute-blog\/2024\/03\/26\/2024-ai-initiative-trends-2\/","title":{"rendered":"AI Initiative Trends for 2024"},"content":{"rendered":"

AI<\/strong> Initiative<\/h1>\n

2024 Trends<\/h2>\n

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In 2024, five key AI-driven wellness trends showcase advancements in personal health management through wearables, hyper-personalization in hospitality and advancements in biotechnology and medicine. These trends also emphasize AI’s potential to enhance operational efficiency across wellness sectors and stress the importance of ethical considerations in AI’s healthcare applications, following principles first proposed by the World Health Organization. The GWI AI Initiative\u2019s observations and forecasts indicate a future where AI not only improves efficiency and personalization but also ethically integrates developments into wellness and healthcare, highlighting the transformative impact that AI is having across multiple industries.<\/p>\n<\/div>\n<\/div>\n

<\/p>\n

TREND 1:<\/strong>
\nAI and Wellness Data in Personal Health Management<\/h3>\n

The application of AI to wellness data has ushered in a new era of personal health management, where every heartbeat, step and breath can be analyzed to provide personalized, real-time health recommendations. The key themes driving this trend are; the infusion of wellness data into lifestyle, the integration of wellness data across data sources, and the communication with data.<\/em><\/strong><\/span><\/p>\n

First,<\/strong> several recent advances signal a greater infusion of wellness data into our lifestyle. Recently, a new piece of furniture\u2014the unassuming Alter mirror\u2014made its debut, offering in-home coaching based on genes and biometrics. Not only does the mirror provide coaching based on the asynchronous collection of genetic biomarkers, but it also offers real-time feedback, with exercise recognition and form correction provided by ASENSEI\u2019s motion recognition technology. As more data-driven products enter the home, we expect consumers to become more fluent in the language of data and biomarkers.<\/span>\u00a0<\/span><\/p>\n

Next,<\/strong> as the Alter mirror illustrates, wellness data is being integrated across multiple sources to provide more comprehensive recommendations. In particular, as the science underlying circadian rhythms and metabolic health reached consumers, we have recently seen an increase in data sharing between sleep devices and metabolic devices. This was best illustrated by Oura\u2019s announcement in July 2023 to integrate with multiple CGM devices (continuous glucose monitors). Healthcare providers are also starting to use multiple devices to track patients\u2019 health, as illustrated by Kencor health\u2019s use of the Galaxy Watch to help track hyperkalemia in kidney disease patients. We expect the trend of data integration to continue well into 2024.<\/span>\u00a0<\/span><\/p>\n

Finally,<\/strong> recent improvements in chat technologies and generative AI are improving our ability to communicate with data. Whoop has been at the cutting edge of this trend, being the first to incorporate ChatGPT into its wearable device, such that the device itself can generate training plans for its wearers.<\/span>\u00a0<\/span><\/p>\n

TREND 2:<\/strong>
\nHyper-Personalization and the Future of Wellness in Hospitality & Spa<\/span><\/span><\/h3>\n

The hospitality industry is undergoing a significant transformation as guests increasingly seek authentically personalized experiences tailored to their individual preferences and wellness needs, particularly in the luxury market where personalized service is expected. Dynamic personalization integrates AI systems in real time to deliver bespoke wellness experiences for guests in wellness-driven hotels and their spas. Through the use of real-time data and predictive analytics, establishments can anticipate guest needs and preferences, provide personalized amenities and offers, at the right price and appropriate time, enhancing their overall wellbeing and experience during their stay.<\/span>\u00a0<\/span><\/p>\n

By adopting AI-driven dynamic personalization systems, hotels and spas are creating more customized experiences for each guest. Select wellness hotels are collecting data on guests\u2019 preferences prior to arrival, and they use this data to personalize room amenities and services. For example, a guest who indicates a preference for rest and recovery may receive curated essential oils, soothing herbal teas and turndown service in their room. With ample data, dynamic personalization can add a stealth, predictive layer for even greater impact. For instance, by tracking the on-time performance of incoming flights and guests\u2019 expected arrival times, among other data, tools like LasoExperience make it possible to anticipate a guest\u2019s emotional state upon check-in and then personalize offers and amenities. If a guest’s flight has been delayed, the hotel may anticipate that the guest could be feeling stressed or frustrated upon arrival. With this insight, the hotel can preemptively tailor offers and amenities to address the guest’s wellbeing. In the event of an unanticipated early arrival, customized activity or spa offers could be the perfect preemptive fit, filling last minute gaps while the room is readied. AI driven hyper-personalization represents the future of hospitality, wellness and spa.<\/span>\u00a0<\/span><\/p>\n

TREND 3:<\/strong>
\nAI in Biotechnology & Medicine<\/span><\/span><\/h3>\n

Advances in machine learning and computing are accelerating the entire biotechnology pipeline, from drug discovery to clinical decision-making. In the early phases of hypothesis generation, molecular biologists now have access to the fastest and most accurate predictions of protein structure to date, such as AlphaFold and newer Large Language based models, which serve as a starting point for designing experiments with a higher likelihood of success. In the laboratory, advances in robotics and automation have amplified the scale of experiments, allowing thousands of experiments to be run in parallel and analyzed automatically using machine vision. New partnerships between computer hardware companies and biotech companies\u2014highlighted by NVidia\u2019s $50M investment in Recursion Pharmaceuticals\u2014indicate that such computationally intensive machine learning techniques are becoming central to accelerating drug discovery.\u00a0<\/span>\u00a0<\/span><\/p>\n

In the clinic, machine vision is assisting physicians with interpreting medical images, which has traditionally been done by eye. With recent improvements in telehealth and IT, AI is helping to decentralize radiology, improving access and affordability of medical imaging globally. In addition, generative AI is already assisting clinicians with the time-consuming job of writing and summarizing clinical notes, which will help reduce burnout among the clinical workforce.\u00a0<\/span>\u00a0<\/span><\/p>\n

At the bedside, therapies are becoming more precise, driven by improved ability to measure and analyze biomarkers. Combined with improvements in our ability to track symptoms and enroll patients in clinical trials, clinical decision-making is becoming increasingly complex in certain fields of medicine, namely oncology, setting the stage for AI to mine patient data and make recommendations. In addition, advances in computer interfaces and machine learning have enabled the development of implantable neural prostheses, offering new hope to patients with spinal cord injuries and demyelinating diseases. Finally, the FDA\u2019s recent approval of Casgevy, a treatment for sickle cell disease in which patients\u2019 stem cells are modified by genome editing using CRISPR\/Cas9 technology, may pave the way for a future in which therapies can be \u201cprogrammed\u201d specifically for an individual.<\/span>\u00a0<\/span><\/p>\n

TREND 4:<\/strong>
\nAI Improving Operational Efficiency<\/span><\/span><\/h3>\n

AI is playing a significant role in improving operational efficiency across numerous industries, including hospitality, healthcare, gyms and spas, by automating data entry, enhancing customer service and optimizing customer relationship management systems. Companies are increasingly leveraging AI to transform operational challenges into growth opportunities by focusing on core processes and utilizing technologies like computer vision for product quality inspection, text recognition for structuring data, and deep learning algorithms for safety monitoring.<\/span>\u00a0<\/span><\/p>\n

By leveraging AI for automation, businesses are expected to see enhanced quality control, reduction of manual errors, optimized resource allocation and streamlined operations. This has been the impetus behind the flurry of investment recently in companies like Abridge, which is creating AI scribes for healthcare. Coupled with business intelligence for more informed decision making, AI is leading towards improved efficiency, productivity, cost reduction and resource optimization. In 2023, this has been illustrated grimly in the battlefield of Ukraine, where the AI firm Palantir has been providing the government with its most sophisticated AI and machine vision technologies to inform how best to deploy precious resources.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

By leveraging technologies like generative AI, companies can gain deeper insights from data to optimize operations and drive efficiency improvements. The future of AI in the industry involves advancements in autonomous systems and robotics that operate independently to enhance productivity and accuracy. By strategically combining diversified AI tools like BuzzSumo for trend analysis and ChatGPT for content creation, digital content businesses can achieve operational excellence by reducing administrative workload, improving service quality and focusing on strategic tasks that contribute to the bottom line. Diversifying a business\u2019s AI toolkit will be essential for long-term operational competitiveness in the emerging business landscape.<\/span><\/p>\n

TREND 5:<\/strong>
\nEthical Use of AI in Health<\/span><\/span><\/h3>\n

The rapid advancement of AI within the healthcare sector presents both groundbreaking opportunities and significant ethical challenges. As AI technologies become increasingly integral to medical diagnostics, treatment planning and patient care, the need for a robust ethical framework to guide their use has never been more critical. The World Health Organization identified several key principles to ensure the ethical deployment of AI in health, which are being championed and further refined by professional organizations, like the Coalition for Health AI (CHAI); these principles were also echoed by US President Biden\u2019s executive order on the \u201cSafe, Secure, and Trustworthy Development and Use of AI\u201d.\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

The key ethical principles emphasize the importance of human autonomy, wellbeing, transparency, accountability, inclusiveness and sustainability. These principles underscore the significance of aligning AI advancements with ethical standards that prioritize human dignity and the collective welfare of society:<\/span>\u00a0<\/span><\/p>\n

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  1. Protecting Human Autonomy:<\/span><\/b> AI systems should support human decision-making, as they are meant to serve and be subordinate to human judgment, not replace it. It is essential to ensure that AI does not undermine human autonomy or consent.<\/span>\u00a0<\/span><\/li>\n
  2. Promoting Human Wellbeing and Safety and the Public Interest<\/span><\/b>: AI technologies in health should contribute to the overall wellbeing of individuals and communities, ensuring safety and prioritizing the public interest, particularly in health emergencies.<\/span>\u00a0<\/span><\/li>\n
  3. Ensuring Transparency, Explainability and Intelligibility: <\/span><\/b>The workings of AI systems should be transparent and understandable to users and other stakeholders. This involves communicating the capabilities and intentions of AI systems in clear and understandable terms.<\/span>\u00a0<\/span><\/li>\n
  4. Fostering Responsibility and Accountability: <\/span><\/b>There should be clarity about who is responsible for the decisions made by AI systems, with mechanisms in place to ensure accountability for the consequences of those AI systems’ actions and decisions.<\/span>\u00a0<\/span><\/li>\n
  5. Ensuring Fairness, Inclusiveness and Equity: <\/span><\/b>AI in health should be inclusive and accessible to all, irrespective of gender, race, age or economic condition. AI should minimize systemic bias, preventing the exacerbation of health inequalities and striving to reduce such disparities.<\/span>\u00a0<\/span><\/li>\n
  6. Promoting AI that is Responsive and Sustainable:<\/span><\/b> AI systems should be adaptable when circumstances change or when they are not serving the interests of human health, and their development should be consistent with environmental sustainability.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n

    Trustworthy, ethical AI is a key trend underpinning the development and implementation of AI in wellness, and in 2024, we expect to see many more individuals and organizations making a principled commitment to enhancing human health by upholding the highest ethical standards in AI. Importantly, we expect to witness the implementation of robust ethical frameworks into policy by organizations and institutions in 2024.\u00a0<\/span>\u00a0<\/span><\/p>\n ","protected":false},"excerpt":{"rendered":"

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