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<a title="What are the Legal Implications of AI Scribes " href="http://bit.ly/44SwNh7" data-mce-href="http://bit.ly/44SwNh7">AI Medical Scribe: Legal Implications and Regulatory Considerations</a><figure id="figure-d5ltkr3nxbmd4ekqgb"><img id="d5ltkr3nxbmd4ekqgb" src="https://media.gettyimages.com/id/2163506576/photo/ai-laws-and-regulations-concept-hand-typing-on-laptop-with-digital-icons-representing.jpg?b=1&s=2048x2048&w=0&k=20&c=c_bMYf2kKgSZ2cDJntWQH_SG9Hf4bKTTdgydWjw09_M=" alt="AI Laws and Regulations Concept. Hand typing on laptop with digital icons representing artificial intelligence, legal standard, ethics, and regulatory compliance, Technology law and policy, copyright," title="AI Laws and Regulations Concept. Hand typing on laptop with digital icons representing artificial intelligence, legal standard, ethics, and regulatory compliance, Technology law and policy, copyright," class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="click on the text below to read the full article " data-positionX="undefined" data-positionY="undefined" data-fullWidth="false"/><figcaption class="body-normal text-gray-500 mt-2">click on the text below to read the full article </figcaption></figure><a href="http://bit.ly/44SwNh7" data-mce-href="http://bit.ly/44SwNh7">AI medical scribes aim to eliminate human workload during documentation. However, their growing use has resulted in clinicians and administrators raising questions about data security, privacy, governance and transparency. Ensuring compliance with privacy laws and medical standards has always been a legal imperative for any healthcare technology—and AI medical scribes are no exception. Patient comfort and trust must remain central when using this new technology. As regulatory measures evolve across jurisdictions, providers need to stay abreast of practices and procedures to safeguard patient data and maintain accountability. In this article, we explore the key legal considerations, from data privacy to regional regulatory frameworks, how they shape the responsible use of AI medical scribes, and how clinicians can gain confidence in using the world’s most compliant AI medical scribes.</a>

What are the Legal Implications of AI Scribes?

Artificial Intelligence in Healthcare, AI Health, digital healthcare provider, telemedicine, medical technology

<h2>Embracing Digital Mental Health Solutions</h2>The landscape of mental health treatment is undergoing a significant transformation with the advent of digital technologies. Companies like Polaris are at the forefront of this change, developing digital mental health companions that not only provide therapeutic support but also address the growing epidemic of loneliness. These digital companions are revolutionizing how individuals access mental health resources, offering a convenient and personalized approach to care.<figure id="figure-vpebh8z5m7jp92k4"><img id="vpebh8z5m7jp92k4" src="https://cdn.durable.co/blocks/8TZEmNZvdC2dLBYfDsI4oXLohq3qkIrJsoL8wB0nrX8zfdSVQRhG6IhzDVUvUo8A.png" alt="Artificial abstract brain." title="Artificial abstract brain." class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 50% 25%;" data-corners="default" data-aspect="16:9" data-caption="" data-positionX="50%" data-positionY="25%" data-fullWidth="false"/></figure><h3>The Rise of Digital Companions</h3>Digital mental health companions are designed to interact with users in real-time, providing immediate support and guidance. These AI-driven platforms utilize advanced algorithms to understand the user's emotional state, offering tailored advice and coping strategies. By simulating human interaction, these companions create a sense of connection, which is crucial for individuals who may feel isolated or hesitant to seek traditional therapy. <h3>Combating Loneliness Through Technology</h3>Loneliness is a growing concern worldwide, exacerbated by factors like social isolation and the stigma surrounding mental health issues. Polaris's digital companions are particularly effective in combating loneliness by providing users with a constant, empathetic presence. This virtual companionship can significantly reduce feelings of isolation, encouraging users to engage more actively with their surroundings and communities.<figure id="figure-7znvy9mrdm7jp99ep"><img id="7znvy9mrdm7jp99ep" src="https://cdn.durable.co/getty/17P9kvRJLDhBFs4qybzIYL4XNPxjLSXiFDWQAoptZRbm6oJZufTudVyrevtJbRMv.jpeg" alt="loneliness technology" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9;" data-mce-src="https://cdn.durable.co/getty/17P9kvRJLDhBFs4qybzIYL4XNPxjLSXiFDWQAoptZRbm6oJZufTudVyrevtJbRMv.jpeg" data-mce-style="aspect-ratio: 16/9;"></figure><h2>Shaping the Future of Mental Health Treatment</h2>The potential of digital mental health companions extends beyond immediate support. As they continue to evolve, these platforms will play a pivotal role in shaping the future of mental health treatment. By integrating machine learning and data analytics, Polaris can refine their companions to offer even more precise and effective interventions. This evolution will likely lead to more personalized treatment plans, where users receive care tailored to their unique needs and circumstances.<h3>Advantages Over Traditional Therapy</h3>One of the most significant advantages of digital mental health companions is their accessibility. Unlike traditional therapy sessions that require scheduling and travel, these digital solutions are available 24/7, allowing users to seek help whenever they need it. Additionally, they offer anonymity and privacy, which can be appealing to those who are uncomfortable with face-to-face interactions. This accessibility can democratize mental health care, making it available to a broader audience.<figure id="figure-spldpxi1ovm7jp9kc2"><img id="spldpxi1ovm7jp9kc2" src="https://cdn.durable.co/getty/25rV3FxLfAfkZ9c3gv2V5xGVd8irMNniLM8hIriJ83h80QAbed6EqxOchuWeMTAo.jpeg" alt="digital therapy session" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9;" data-mce-src="https://cdn.durable.co/getty/25rV3FxLfAfkZ9c3gv2V5xGVd8irMNniLM8hIriJ83h80QAbed6EqxOchuWeMTAo.jpeg" data-mce-style="aspect-ratio: 16/9;"></figure><h3>Challenges and Considerations</h3>While digital companions offer numerous benefits, there are challenges to consider. Ensuring the security and privacy of user data is paramount, as is maintaining the accuracy and effectiveness of the AI algorithms. Additionally, there is a need for ongoing research to assess the long-term impact of these solutions on mental health outcomes. Polaris is committed to addressing these challenges, investing in continuous development and improvement of their technology.<h2>A Collaborative Approach to Mental Health</h2>Polaris's digital companions are not meant to replace human therapists but rather to complement them. By working alongside traditional treatment methods, these digital tools can enhance overall patient care. Therapists can utilize data generated from interactions with digital companions to gain deeper insights into their patients’ needs and progress, enabling more informed decision-making.In conclusion, Polaris's digital mental health companions are paving the way for innovative treatments that address both mental health and loneliness. As these technologies continue to advance, they hold the promise of transforming how individuals engage with their mental well-being, leading to more accessible, effective, and holistic care solutions.

The Role of Polaris's Digital Mental Health Companions in Shaping Future Treatment and Combating Loneliness

AlphaFold 3 predicts the structure and interactions of all of life’s molecules. May 08, 2024Introducing AlphaFold 3, a new AI model developed by Google DeepMind and Isomorphic Labs. By accurately predicting the structure of proteins, DNA, RNA, ligands and more, and how they interact, we hope it will transform our understanding of the biological world and drug discovery. Inside every plant, animal and human cell are billions of molecular machines. They’re made up of proteins, DNA and other molecules, but no single piece works on its own. Only by seeing how they interact together, across millions of types of combinations, can we start to truly understand life’s processes.In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy.We hope AlphaFold 3 will help transform our understanding of the biological world and drug discovery. Scientists can access the majority of its capabilities, for free, through our newly launched AlphaFold Server, an easy-to-use research tool. To build on AlphaFold 3’s potential for drug design, Isomorphic Labs is already collaborating with pharmaceutical companies to apply it to real-world drug design challenges and, ultimately, develop new life-changing treatments for patients.Our new model builds on the foundations of AlphaFold 2, which in 2020 made a fundamental breakthrough in protein structure prediction. So far, millions of researchers globally have used AlphaFold 2 to make discoveries in areas including malaria vaccines, cancer treatments and enzyme design. AlphaFold has been cited more than 20,000 times and its scientific impact recognized through many prizes, most recently the Breakthrough Prize in Life Sciences. AlphaFold 3 takes us beyond proteins to a broad spectrum of biomolecules. This leap could unlock more transformative science, from developing biorenewable materials and more resilient crops, to accelerating drug design and genomics research. &nbsp;<figure id="figure-0liec3x1jcgmlvyckfsh" class=""><img id="0liec3x1jcgmlvyckfsh" src="https://cdn.durable.co/blocks/25l7QgtUCKvfmQg3MDs2vBIApOxgeE4LOqRVpmECrw8O7AuzzxPNZDdz9iwJWLd2.png" alt="books on brown wooden shelf" title="books on brown wooden shelf" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 20% 50%;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="20%" data-positiony="50%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/25l7QgtUCKvfmQg3MDs2vBIApOxgeE4LOqRVpmECrw8O7AuzzxPNZDdz9iwJWLd2.png" data-mce-style="aspect-ratio: 16/9; object-position: 20% 50%;"></figure> 7PNM - Spike protein of a common cold virus (Coronavirus OC43): AlphaFold 3’s structural prediction for a spike protein (blue) of a cold virus as it interacts with antibodies (turquoise) and simple sugars (yellow), accurately matches the true structure (gray). The animation shows the protein interacting with an antibody, then a sugar. Advancing our knowledge of such immune-system processes helps better understand coronaviruses, including COVID-19, raising possibilities for improved treatments.How AlphaFold 3 reveals life’s molecules Given an input list of molecules, AlphaFold 3 generates their joint 3D structure, revealing how they all fit together. It models large biomolecules such as proteins, DNA and RNA, as well as small molecules, also known as ligands — a category encompassing many drugs. Furthermore, AlphaFold 3 can model chemical modifications to these molecules which control the healthy functioning of cells, that when disrupted can lead to disease.AlphaFold 3’s capabilities come from its next-generation architecture and training that now covers all of life’s molecules. At the core of the model is an improved version of our Evoformer module — a deep learning architecture that underpinned AlphaFold 2’s incredible performance. After processing the inputs, AlphaFold 3 assembles its predictions using a diffusion network, akin to those found in AI image generators. The diffusion process starts with a cloud of atoms, and over many steps converges on its final, most accurate molecular structure.AlphaFold 3’s predictions of molecular interactions surpass the accuracy of all existing systems. As a single model that computes entire molecular complexes in a holistic way, it’s uniquely able to unify scientific insights.<figure id="figure-st67f3h31dlvycp8n1" class=""><img id="st67f3h31dlvycp8n1" src="https://cdn.durable.co/blocks/dxtCOpY9rlEmHivErmPcu4ZUblsSWurZETifPMSJeqneeeWI2BvwByUj2oWBkCbo.png" alt="boy in green sweater writing on white paper" title="boy in green sweater writing on white paper" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/dxtCOpY9rlEmHivErmPcu4ZUblsSWurZETifPMSJeqneeeWI2BvwByUj2oWBkCbo.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure>7R6R - DNA binding protein: AlphaFold 3’s prediction for a molecular complex featuring a protein (blue) bound to a double helix of DNA (pink) is a near-perfect match to the true molecular structure discovered through painstaking experiments (gray).Leading drug discovery at Isomorphic Labs AlphaFold 3 creates capabilities for drug design with predictions for molecules commonly used in drugs, such as ligands and antibodies, that bind to proteins to change how they interact in human health and disease.AlphaFold 3 achieves unprecedented accuracy in predicting drug-like interactions, including the binding of proteins with ligands and antibodies with their target proteins. AlphaFold 3 is 50% more accurate than the best traditional methods on the PoseBusters benchmark without needing the input of any structural information, making AlphaFold 3 the first AI system to surpass physics-based tools for biomolecular structure prediction. The ability to predict antibody-protein binding is critical to understanding aspects of the human immune response and the design of new antibodies — a growing class of therapeutics.Using AlphaFold 3 in combination with a complementary suite of in-house AI models, Isomorphic Labs is working on drug design for internal projects as well as with pharmaceutical partners. Isomorphic Labs is using AlphaFold 3 to accelerate and improve the success of drug design — by helping understand how to approach new disease targets, and developing novel ways to pursue existing ones that were previously out of reach.AlphaFold Server: A free and easy-to-use research tool<figure id="figure-y67iqapl5olvycr8qt" class=""><img id="y67iqapl5olvycr8qt" src="https://cdn.durable.co/blocks/2dwUZ1daT20Lvl4tcQPezwsB6eSJKZgCZiGvAsRNqWsaJS6v2uNqc0ruBj5D0ZdF.png" alt="person writing on brown wooden table near white ceramic mug" title="person writing on brown wooden table near white ceramic mug" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/2dwUZ1daT20Lvl4tcQPezwsB6eSJKZgCZiGvAsRNqWsaJS6v2uNqc0ruBj5D0ZdF.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure>8AW3 - RNA modifying protein: AlphaFold 3’s prediction for a molecular complex featuring a protein (blue), a strand of RNA (purple), and two ions (yellow) closely matches the true structure (gray). This complex is involved with the creation of other proteins — a cellular process fundamental to life and health. Google DeepMind’s newly launched AlphaFold Server is the most accurate tool in the world for predicting how proteins interact with other molecules throughout the cell. It is a free platform that scientists around the world can use for non-commercial research. With just a few clicks, biologists can harness the power of AlphaFold 3 to model structures composed of proteins, DNA, RNA and a selection of ligands, ions and chemical modifications.AlphaFold Server helps scientists make novel hypotheses to test in the lab, speeding up workflows and enabling further innovation. Our platform gives researchers an accessible way to generate predictions, regardless of their access to computational resources or their expertise in machine learning.Experimental protein-structure prediction can take about the length of a PhD and cost hundreds of thousands of dollars. Our previous model, AlphaFold 2, has been used to predict hundreds of millions of structures, which would have taken hundreds of millions of researcher-years at the current rate of experimental structural biology. "With AlphaFold Server, it’s not only about predicting structures anymore, it’s about generously giving access: allowing researchers to ask daring questions and accelerate discoveries." Céline Bouchoux The Francis Crick Institute &nbsp; Sharing the power of AlphaFold 3 responsibly With each AlphaFold release, we’ve sought to understand the broad impact of the technology, working together with the research and safety community. We take a science-led approach and have conducted extensive assessments to mitigate potential risks and share the widespread benefits to biology and humanity.Building on the external consultations we carried out for AlphaFold 2, we’ve now engaged with more than 50 domain experts, in addition to specialist third parties, across biosecurity, research and industry, to understand the capabilities of successive AlphaFold models and any potential risks. We also participated in community-wide forums and discussions ahead of AlphaFold 3’s launch.AlphaFold Server reflects our ongoing commitment to share the benefits of AlphaFold, including our free database of 200 million protein structures. We’ll also be expanding our free AlphaFold education online course with EMBL-EBI and partnerships with organizations in the Global South to equip scientists with the tools they need to accelerate adoption and research, including on underfunded areas such as neglected diseases and food security. We’ll continue to work with the scientific community and policy makers to develop and deploy AI technologies responsibly.Opening up the future of AI-powered cell biology <figure id="figure-k9qts0ak6lvyctbuo" class=""><img id="k9qts0ak6lvyctbuo" src="https://cdn.durable.co/blocks/17Vq9TdkM9w6nbjv21Nqw8j2Gj2ksx9skoTGfKCpZrRAw1sYb93AHDSwliTzwe18.png" alt="MacBook Pro near white open book" title="MacBook Pro near white open book" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg selected" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/17Vq9TdkM9w6nbjv21Nqw8j2Gj2ksx9skoTGfKCpZrRAw1sYb93AHDSwliTzwe18.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;" data-mce-selected="1"></figure>7BBV - Enzyme: AlphaFold 3’s prediction for a molecular complex featuring an enzyme protein (blue), an ion (yellow sphere) and simple sugars (yellow), along with the true structure (gray). This enzyme is found in a soil-borne fungus (Verticillium dahliae) that damages a wide range of plants. Insights into how this enzyme interacts with plant cells could help researchers develop healthier, more resilient crops. AlphaFold 3 brings the biological world into high definition. It allows scientists to see cellular systems in all their complexity, across structures, interactions and modifications. This new window on the molecules of life reveals how they’re all connected and helps understand how those connections affect biological functions — such as the actions of drugs, the production of hormones and the health-preserving process of DNA repair.The impacts of AlphaFold 3 and our free AlphaFold Server will be realized through how they empower scientists to accelerate discovery across open questions in biology and new lines of research. We’re just beginning to tap into AlphaFold 3’s potential and can’t wait to see what the future holds.... End&nbsp;

AlphaFold 3 predicts the structure and interactions of all of life’s molecules

<h2>Introduction</h2>In recent years, the healthcare industry has been undergoing a transformative shift towards leveraging cutting-edge technologies to address not only physical health but also mental and emotional well-being. One pressing issue that has come to the forefront is the loneliness epidemic, affecting individuals of all ages and backgrounds. However, with the emergence of generative AI technology, there is newfound hope for tackling this pervasive problem in innovative ways.<h3>The Impact of Loneliness on Health</h3>Loneliness is more than just a feeling of isolation; it can have serious implications for both mental and physical health. Studies have shown that chronic loneliness is associated with a higher risk of developing conditions such as depression, anxiety, cardiovascular diseases, and even premature death.<h3>Understanding Generative AI</h3>Generative AI, a subset of artificial intelligence, is revolutionizing the way we approach healthcare by enabling machines to create content autonomously. This technology has the potential to generate personalized interventions that cater to individuals' unique needs and preferences, making it a powerful tool in combating loneliness.<figure id="figure-wwmuwzy8wplvla3zh8"><img id="wwmuwzy8wplvla3zh8" src="https://images.unsplash.com/photo-1617992799119-5d0ed1fb9379?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wyNjI5NjF8MHwxfHNlYXJjaHwxNnx8Z2VuZXJhdGl2ZSUyMEFJJTIwbG9uZWxpbmVzc3xlbnwwfHx8fDE3MTQ0MTQ0NzB8MA&amp;ixlib=rb-4.0.3&amp;q=80&amp;w=1080" alt="generative AI loneliness" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9;" data-mce-src="https://images.unsplash.com/photo-1617992799119-5d0ed1fb9379?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wyNjI5NjF8MHwxfHNlYXJjaHwxNnx8Z2VuZXJhdGl2ZSUyMEFJJTIwbG9uZWxpbmVzc3xlbnwwfHx8fDE3MTQ0MTQ0NzB8MA&amp;ixlib=rb-4.0.3&amp;q=80&amp;w=1080" data-mce-style="aspect-ratio: 16/9;"></figure><h3>Personalized Interventions</h3>One of the key advantages of harnessing generative AI in healthcare is its ability to create tailored interventions for individuals struggling with loneliness. By analyzing vast amounts of data, including social interactions, interests, and communication patterns, AI algorithms can generate personalized recommendations for fostering meaningful connections.<h3>Virtual Companionship</h3>Generative AI can also be utilized to develop virtual companionship programs that provide individuals with round-the-clock support and companionship. These AI-powered companions can engage in conversations, offer emotional support, and even facilitate social interactions, helping to alleviate feelings of loneliness and isolation.<figure id="figure-e03yhfmfpmlvla41mv"><img id="e03yhfmfpmlvla41mv" src="https://images.unsplash.com/photo-1644428239740-b8295bfef42f?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wyNjI5NjF8MHwxfHNlYXJjaHw5fHx2aXJ0dWFsJTIwY29tcGFuaW9uc2hpcCUyMGxvbmVsaW5lc3N8ZW58MHx8fHwxNzE0NDE0NDcyfDA&amp;ixlib=rb-4.0.3&amp;q=80&amp;w=1080" alt="virtual companionship loneliness" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9;" data-mce-src="https://images.unsplash.com/photo-1644428239740-b8295bfef42f?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wyNjI5NjF8MHwxfHNlYXJjaHw5fHx2aXJ0dWFsJTIwY29tcGFuaW9uc2hpcCUyMGxvbmVsaW5lc3N8ZW58MHx8fHwxNzE0NDE0NDcyfDA&amp;ixlib=rb-4.0.3&amp;q=80&amp;w=1080" data-mce-style="aspect-ratio: 16/9;"></figure><h3>Community Building</h3>Another exciting application of generative AI in tackling the loneliness epidemic is in community building. By analyzing social networks and identifying individuals who may be at risk of loneliness, AI algorithms can facilitate the creation of supportive communities where members can connect, share experiences, and provide mutual support.<h3>Empowering Healthcare Providers</h3>Generative AI technology not only benefits individuals directly but also empowers healthcare providers to deliver more personalized and effective care. By leveraging AI-generated insights and recommendations, healthcare professionals can tailor their interventions to better address the underlying causes of loneliness and improve patient outcomes.<h3>Ethical Considerations</h3>While the potential of generative AI in combating the loneliness epidemic is vast, it is crucial to consider the ethical implications of using such technology in healthcare. Safeguarding data privacy, ensuring transparency in AI decision-making processes, and maintaining human oversight are essential to uphold ethical standards and protect individuals' well-being.<h3>The Future of Healthcare</h3>As we continue to harness the power of generative AI to tackle the loneliness epidemic, we are witnessing a paradigm shift in the way we approach mental health and well-being. By combining technological innovation with human empathy, we have the opportunity to revolutionize healthcare and create a more connected and supportive society for all.<figure id="figure-mb09ubmreplvla462b"><img id="mb09ubmreplvla462b" src="https://images.unsplash.com/photo-1579684385127-1ef15d508118?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wyNjI5NjF8MHwxfHNlYXJjaHwxOHx8ZnV0dXJlJTIwb2YlMjBoZWFsdGhjYXJlfGVufDB8fHx8MTcxNDQxNDQ3OHww&amp;ixlib=rb-4.0.3&amp;q=80&amp;w=1080" alt="future of healthcare" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9;" data-mce-src="https://images.unsplash.com/photo-1579684385127-1ef15d508118?crop=entropy&amp;cs=tinysrgb&amp;fit=max&amp;fm=jpg&amp;ixid=M3wyNjI5NjF8MHwxfHNlYXJjaHwxOHx8ZnV0dXJlJTIwb2YlMjBoZWFsdGhjYXJlfGVufDB8fHx8MTcxNDQxNDQ3OHww&amp;ixlib=rb-4.0.3&amp;q=80&amp;w=1080" data-mce-style="aspect-ratio: 16/9;"></figure>

Revolutionising Healthcare: Harnessing Generative AI to Tackle the Loneliness Epidemic

Polypharmacy, or the concurrent use of five or more medications, is common in older adults and increases the risk of adverse drug interactions. While deprescribing unnecessary drugs can combat this risk, the decision-making process can be complex and time-consuming. Increasingly, there is a need for effective polypharmacy management tools that can support short-staffed primary care practitioners.In a new study, researchers from the Mass General Brigham MESH Incubator found that ChatGPT, a generative artificial intelligence (AI) chatbot, showed promise as a tool to manage polypharmacy and deprescription. These findings, published April 18th in the Journal of Medical Systems, demonstrate the first use case of AI models in medicine management.To evaluate its utility, the investigators provided ChatGPT with different clinical scenarios and asked it a set of decision-making questions. Each scenario featured the same elderly patient taking a mixture of medications but included variations in cardiovascular disease history (CVD) and degree of impairment in activities of daily living (ADL).When asked yes or no questions about reducing prescribed drugs, ChatGPT consistently recommended deprescribing medications in patients without a history of CVD. However, it was more cautious when overlying CVD was introduced, and more likely to keep the patient’s medication regimen unchanged. In both cases, the researchers observed that ADL impairment severity did not seem to affect decision outcomes.The team also noted that ChatGPT had a tendency to disregard pain and favored deprescribing pain medications over other drug types like statins or antihypertensives. In addition, ChatGPT responses varied when presented with the same scenario in new chat sessions — which the authors suggest could reflect inconsistency in commonly reported clinical deprescribing trends on which the model was trained.More than 40 percent of older adults meet the criteria for polypharmacy. The rate of seniors on Medicare seeing more specialists on their care teams has increased in recent years, leaving primary care providers to oversee medication management. An effective AI tool could help aid this practice, according to the researchers.&nbsp;“Our study provides the first use case of ChatGPT as a clinical support tool for medication management,” said senior corresponding author Marc Succi, MD, Associate Chair of Innovation and Commercialization at Mass General Brigham Radiology and Executive Director of the MESH Incubator. “While caution should be taken to increase accuracy of such models, AI-assisted polypharmacy management could help alleviate the increasing burden on general practitioners. Further research with specifically trained AI tools may significantly enhance the care of aging patients.”Arya Rao, lead author, MESH researcher and Harvard Medical student, added “Our findings suggest that AI-based tools can play an important role in ensuring safe medication practices for older adults; it is imperative that we continue to refine these tools to account for the complexities of medical decision-making.”Read more in the Journal of Medical Systems.

Study Finds ChatGPT Shows Promise as Medication Management Tool, Could Help Improve Geriatric Health Care

NEWS 21 March 2024 Google AI could soon use a person’s cough to diagnose disease Machine-learning system trained on millions of human audio clips shows promise for detecting COVID-19 and tuberculosis. By Mariana Lenharo <figure id="figure-p6fe6b3s57lur44iyu"><img id="p6fe6b3s57lur44iyu" src="https://cdn.durable.co/blocks/15M2ZWOpgNsMqODyjSIslp5sq384SEhPOK4f3DSjr4tmbnQoWYP0sx9i2fPYaEYn.png" alt="person holding pencil near laptop computer" title="person holding pencil near laptop computer" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/15M2ZWOpgNsMqODyjSIslp5sq384SEhPOK4f3DSjr4tmbnQoWYP0sx9i2fPYaEYn.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;"></figure> &nbsp;The field of audiomics combines artificial-intelligence tools with human sounds, such as coughs, to evaluate health.Credit: Getty A team led by Google scientists has developed a machine-learning tool that can help to detect and monitor health conditions by evaluating noises such as coughing and breathing. The artificial intelligence (AI) system1, trained on millions of audio clips of human sounds, might one day be used by physicians to diagnose diseases including COVID-19 and tuberculosis and to assess how well a person’s lungs are functioning. &nbsp;AI detects eye disease and risk of Parkinson’s from retinal images This is not the first time a research group has explored using sound as a biomarker for disease. The concept gained traction during the COVID-19 pandemic, when scientists discovered that it was possible to detect the respiratory disease through a person’s cough2.What’s new about the Google system — called Health Acoustic Representations (HeAR) — is the massive data set that it was trained on, and the fact that it can be fine-tuned to perform multiple tasks.The researchers, who reported the tool earlier this month in a preprint1 that has not yet been peer reviewed, say it’s too early to tell whether HeAR will become a commercial product. For now, the plan is to give interested researchers access to the model so that they can use it in their own investigations. “Our goal as part of Google Research is to spur innovation in this nascent field,” says Sujay Kakarmath, a product manager at Google in New York City who worked on the project.How to train your model Most AI tools being developed in this space are trained on audio recordings — for example, of coughs — that are paired with health information about the person who made the sounds. For example, the clips might be labelled to indicate that the person had bronchitis at the time of the recording. The tool comes to associate features of the sounds with the data label, in a training process called supervised learning. Google AI has better bedside manner than human doctors — and makes better diagnoses “In medicine, traditionally, we have been using a lot of supervised learning, which is great because you have a clinical validation,” says Yael Bensoussan, a laryngologist at the University of South Florida in Tampa. “The downside is that it really limits the data sets that you can use, because there is a lack of annotated data sets out there.”Instead, the Google researchers used self-supervised learning, which relies on unlabelled data. Through an automated process, they extracted more than 300 million short sound clips of coughing, breathing, throat clearing and other human sounds from publicly available YouTube videos.Each clip was converted into a visual representation of sound called a spectrogram. Then the researchers blocked segments of the spectrograms to help the model learn to predict the missing portions. This is similar to how the large language model that underlies chatbot ChatGPT was taught to predict the next word in a sentence after being trained on myriad examples of human text. Using this method, the researchers created what they call a foundation model, which they say can be adapted for many tasks.An efficient learner In the case of HeAR, the Google team adapted it to detect COVID-19, tuberculosis and characteristics such as whether a person smokes. Because the model was trained on such a broad range of human sounds, to fine-tune it, the researchers only had to feed it very limited data sets labelled with these diseases and characteristics.On a scale where 0.5 represents a model that performs no better than a random prediction and 1 represents a model that makes an accurate prediction each time, HeAR scored 0.645 and 0.710 for COVID-19 detection, depending on which data set it was tested on — a better performance than existing models trained on speech data or general audio. For tuberculosis, the score was 0.739. &nbsp;An AI revolution is brewing in medicine. What will it look like? The fact that the original training data were so diverse — with varying sound quality and human sources — also means that the results are generalizable, Kakarmath says.Ali Imran, an engineer at the University of Oklahoma in Tulsa, says that the sheer volume of data used by Google lends significance to the research. “It gives us the confidence that this is a reliable tool,” he says.Imran leads the development of an app named AI4COVID-19, which has shown promise at distinguishing COVID-19 coughs from other types of cough3. His team plans to apply for approval from the US Food and Drug Administration (FDA) so that the app can eventually move to market; he is currently seeking funding to conduct the necessary clinical trials. So far, no FDA-approved tool provides diagnosis through sounds.The field of health acoustics, or ‘audiomics’, is promising, Bensoussan says. “Acoustic science has existed for decades. What’s different is that now, with AI and machine learning, we have the means to collect and analyse a lot of data at the same time.” She co-leads a research consortium focused on exploring voice as a biomarker to track health.“There’s an immense potential not only for diagnosis, but also for screening” and monitoring, she says. “We can’t repeat scans or biopsies every week. So that’s why voice becomes a really important biomarker for disease monitoring,” she adds. “It’s not invasive, and it’s low resource.” Nature 628, 19-20 (2024)doi: https://doi.org/10.1038/d41586-024-00869-0References Baur, S. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2403.02522 (2024). Santosh, K. C., Rasmussen, N., Mamun, M. &amp; Aryal, S. PeerJ. Comput. Sci. 8, e958 (2022).Article PubMed Google Scholar&nbsp; Imran, A. et al. Inform. Med. Unlocked 20, 100378 (2020).Article PubMed Google Scholar

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