AI2MED’s Post

Regard using AI to maximise the value from patient data Regard, a Los Angeles-based clinical decision software company, has secured $61 million in Series B funding to advance its AI-powered clinical-insights platform, scale its reach in healthcare, and invest in research on healthcare LLMs. Regard's tool utilizes AI to evaluate patient history, generate clinical decisions and documentation, and facilitate clinician-to-clinician communications. This is important because the World Economic Forum estimates that less than 3% of patient data is used. Unlocking the value in the extra 97% is a huge opportunity to improve healthcare outcomes for patients and support the clinicians. This is yet another example of the continued investment in AI for the health sector, and the innovations we can expect in the future. While not the only company doing this work, It's great to see Regard pushing the boundaries of what's possible with AI in healthcare. I can't wait to see what innovations they come up with next! Congratulations to Eli Ben-Joseph and the team at Regard on the success of the funding round. https://lnkd.in/ggD2NpX7

https://lnkd.in/gWiU6ECR   Generative AI is set to transform the healthcare landscape by offering unprecedented potential to enhance medical administration processes, healthcare delivery, diagnostics and patient management. In 2023, researchers estimated that the technology could represent a $5 billion to $13 billion opportunity in the healthcare sector by 2030.   Given this exciting potential, many healthcare organisations across Australia are taking note of what leading healthcare institutions are doing with generative AI in an effort to understand its potential impact. They are especially concerned with addressing issues such as data privacy, ethical concerns and the need for an evidence base as a foundation for trusting AI-generated outcomes before the technology is more widely embraced in clinical settings.   In response to such concerns, the Victorian Government recently introduced a mandate against the use of generative AI in clinical settings. Against this backdrop, Austin Health, a leader in healthcare innovation, is meticulously working to build a strong evidence base on the transformative potential of AI in enhancing healthcare outcomes while adhering to the highest standards of patient safety. Its journey reflects a strategic, responsible exploration of generative AI’s capabilities, setting a benchmark for others in the healthcare sector.   Austin Health Victoria State Government (Victorian Government) Microsoft   Blog – Healthcare – Transformation through the leveraging of Data and AI – Google - https://lnkd.in/g5vh8ndC    

What precautions should be taken when relying on AI for healthcare insights? When relying on AI for healthcare insights, several precautions should be taken to ensure safety, effectiveness, and ethical use: Data Privacy and Security: Ensure compliance with regulations like HIPAA (in the U.S.) to protect patient confidentiality. Implement robust data security measures to safeguard sensitive health information. Bias Mitigation: Actively work to identify and mitigate biases in AI algorithms. Diverse and representative training datasets are essential to avoid perpetuating health disparities and ensuring equitable treatment. Human Oversight: Always involve healthcare professionals in the decision-making process. AI should augment, not replace, human expertise. Clinicians should review AI-generated insights before making clinical decisions. Validation and Testing: Thoroughly validate AI models using diverse datasets and conduct rigorous testing in clinical settings to ensure accuracy and reliability before deployment. Transparency: Foster transparency in AI systems, making it clear how insights are generated. This includes understanding the underlying algorithms and data sources, which is crucial for building trust among healthcare providers and patients. Continuous Monitoring and Evaluation: Regularly monitor AI performance and outcomes in real-world settings to identify any issues or unintended consequences. Adjust models as needed based on feedback and new data. Patient Consent and Engagement: Ensure that patients are informed about how AI is used in their care and obtain consent where necessary. Engaging patients in the process can enhance trust and understanding. Interdisciplinary Collaboration: Promote collaboration between data scientists, healthcare professionals, ethicists, and patients to ensure a holistic approach to AI development and implementation. Focus on Explainability: Prioritize the development of explainable AI models that can clearly communicate the rationale behind their recommendations. This is important for clinician understanding and patient trust. Regulatory Compliance: Stay informed about and comply with relevant regulations and standards governing the use of AI in healthcare, which may evolve over time as technology advances. Education and Training: Provide training for healthcare professionals on how to effectively use AI tools, including understanding their limitations and interpreting results. By taking these precautions, healthcare organizations can harness the benefits of AI while minimizing risks and ensuring ethical, responsible use in patient care.

💻 We have published recommendations to improve the uptake of AI in healthcare, with a focus on clinical decision-making AI systems 🩺 European doctors note that the uptake of AI in healthcare is currently low due to several factors, including the complex environment of the sector, the wide range of products available on the market, the majority of which are not certified by a third-party, and a lack of confidence in using AI systems based on data from unknown data sources or on data collection processes. 🗣️ CPME President Dr Christiaan Keijzer said “The main purpose for the integration of AI in healthcare should be the improvement of clinical practice, therefore technology needs to be embedded in clinical pathways. Those developing the digital tools need to learn the real needs of healthcare professionals, patients and their carers and guardians. “AI products should be seamlessly integrated into the healthcare information system. We must avoid situations where they function as standalone tools requiring healthcare providers to manually input the same information across different systems. This is inefficient and causes frustration and administrative burnout. “European doctors stress the importance of publicly coordinated efforts to establish knowledge environments of sufficient scale and clinical expertise within national settings. This coordination is crucial to support sustained AI research collaboration at both the EU and national levels.” 🗣️ CPME Vice President Prof. Dr Ray Walley said “The deployment of AI cannot mean a disinvestment in other areas of healthcare systems. Short-term needs should be exploited first. AI should be used to resolve inefficiencies in healthcare provisions, knowledge fragmentation and automatisation of time-intensive routine processes. He added “Doctors should be free to decide whether to use an AI system, without repercussions, bearing in mind the best interests of the patient, and to retain the right to disagree with an AI system.” 👉 Read more: https://lnkd.in/e2WhjaZM

book that made an impact on me recently: “AI in Healthcare: The Hope, The Hype, The Promise, The Peril” by Peter D. Kress offers a comprehensive examination of artificial intelligence’s role in modern medical settings. The book delves into the transformative potential of AI in healthcare, highlighting its capacity to enhance diagnostics, personalize treatment plans, and streamline administrative tasks. Kress also addresses the challenges and ethical considerations associated with AI adoption, such as data privacy concerns, the risk of algorithmic bias, and the importance of maintaining the human element in patient care. Through a balanced analysis, the author provides insights into how healthcare professionals can navigate the integration of AI technologies to maximize benefits while mitigating risks. For instance, AI has been instrumental in supporting clinicians by reducing administrative burdens. At Kaiser Permanente, AI-powered clinical scribe technology drafts clinical notes from patient encounter recordings, allowing physicians to focus more on patient care. However, experts caution against over-reliance on AI for clinical decision-making, emphasizing that these tools should augment rather than automate such processes. The book also discusses the ethical implications of AI in healthcare, including concerns about data privacy and the potential for AI systems to perpetuate existing biases if not properly managed. Kress advocates for a thoughtful approach to AI integration, ensuring that technological advancements do not compromise patient trust or equity in care delivery. Overall, “AI in Healthcare” serves as a valuable resource for healthcare professionals, policymakers, and technologists seeking to understand the nuanced landscape of AI applications in medicine.

The impact of DeepSeek R1 in healthcare will be significantly influenced by several key dependencies https://lnkd.in/eCf-Wt2F Data Availability and Quality High-Quality Data: The availability of high-quality, diverse, and unbiased healthcare data is crucial. This includes electronic health records, genomic data, medical images, and other relevant information. Data Privacy and Security: Robust data privacy and security measures are essential to protect sensitive patient information and build trust in AI-powered healthcare solutions. Technological Advancements: Computational Power: Continued advancements in computing power will be necessary to support the computational demands of training and deploying complex AI models like DeepSeek R1. Algorithm Development: Ongoing research and development in AI algorithms, including reinforcement learning and other cutting-edge techniques, will be critical for further improving the performance and capabilities of DeepSeek R1. Regulatory Landscape: Clear Regulations: Clear and flexible regulations are needed to guide the development and deployment of AI in healthcare, ensuring patient safety, data privacy, and ethical considerations. Regulatory Agility: The regulatory framework must be agile and adaptable to keep pace with the rapid advancements in AI technology. Ethical Considerations: Addressing Bias: Mitigating bias in AI algorithms is crucial to ensure fair and equitable access to healthcare for all individuals. Transparency and Explainability: Developing AI models that are transparent and explainable is essential for building trust and ensuring that clinicians and patients understand how AI-powered decisions are made. Human Oversight: Maintaining appropriate levels of human oversight in AI-powered healthcare systems is crucial to ensure safety, ethical decision-making, and the responsible use of technology. These dependencies highlight the complex interplay of factors that will shape the future impact of DeepSeek R1 in healthcare. https://lnkd.in/eCf-Wt2F Nelson Advisors work with Healthcare Technology Founders, Owners and Investors to assess whether they should 'Build, Buy, Partner or Sell' in order to maximise shareholder value > https://lnkd.in/eXk-gR-k   Healthcare Technology Thought Leadership from Nelson Advisors – Market Insights, Analysis & Predictions. Visit https://lnkd.in/ezyUh5i    Buy Side, Sell Side, Growth & Strategy services for HealthTech Founders, Owners and Investors. Email lloyd@nelsonadvisors.co.uk   Nelson Advisors Healthcare Technology Newsletter > Mergers, Acquisitions, Growth, Strategy, Insights & Predictions. Subscribe Today! https://lnkd.in/e5hTp_xb    #DeepSeek #R1 #DeepSeekR1 #HealthTech #DigitalHealth #HealthIT #NelsonAdvisors #Mergers #Acquisitions #Growth #Strategy #Innovation #NHS #VentureCapital #PrivateEquity

Forbes Technology Council recently highlighted a few areas where #AI can change healthcare, including improving interoperability, reducing administrative burden and embedding fairness and transparency in AI-driven healthcare. In addition to these improvements AI can help with, the article notes that AI supports the implementation of a value-based system. One of the benefits that the use of Generative AI in healthcare brings is the ability to capture real-time data that allows healthcare providers to adopt personalized, outcome-focused care plans for patients. Some healthcare systems are even using AI to identify high-risk patients early on, and provide preventive measures for patients to begin treatment faster. The future of healthcare and patient-focused care will thrive with innovative solutions like Generative AI as they bolster more efficiency in the healthcare system and can provide lifesaving information in real-time. https://lnkd.in/gC7akpz5

<AI Generated> 3 Key Takeaways from "Healthcare AI's Elusive ROI" by Spencer Dorn - High Expectations, Limited Returns: Despite the promise of AI revolutionizing healthcare, achieving a positive return on investment (ROI) remains difficult. Many AI tools, especially in clinical settings, are still prone to errors and inefficiencies, preventing the expected financial gains. - Operational Hurdles: AI implementation often adds complexity rather than streamlining processes. For instance, automating tasks like documentation and patient communication sometimes increases the workload for healthcare providers, which paradoxically reduces productivity. - Incremental Gains Over Quick Wins: Healthcare organizations should temper expectations and focus on small, incremental improvements from AI rather than seeking revolutionary changes. AI may be more suited to administrative tasks like revenue cycle management, rather than high-stakes clinical applications for now. These insights remind us that while AI holds great potential, a long-term, cautious approach is necessary for realizing meaningful benefits in healthcare. </AI Generated> <Human> Interestingly, it missed the point of the article - that ROI is hard to quantify. Nothing in the AI summary was wrong; it just buried the lead. AI (and many other healthcare technologies) are hard to evaluate. Who is the user? Who is paying? How do they perceive ROI? As a patient, I'm not paying, at least not directly, for the AI, and it may or may not have value for me. Also, what is the expected time frame for ROI? It shouldn't be immediate. And lastly, what's the baseline you are comparing against? Is that accurate? Evaluating GenAI is interesting. They do really well on structured medical exams, which are the typical standard, but do less well in the "wild". Also, numerous studies evaluate mistakes by GenAI. What about the human mistakes? </Human> https://lnkd.in/g5pnvD5Z #roi #ai #llms #medical #healthcare

Balancing AI’s Promise and Challenges in Healthcare Someone recently told me that I seem skeptical of AI. And, to an extent, they’re right. I am skeptical, not of AI itself, but of the way it’s often treated as a universal solution or a synonym for “progress,” as though introducing it is automatically a step forward. In healthcare, this mindset is especially concerning. While AI has enormous potential to improve diagnostics, optimize resource allocation, and streamline operations, it’s not without significant challenges. For instance: ▪️Who controls and safeguards the data used to train AI systems? ▪️How do we ensure accountability and fairness in AI decisions? ▪️Can AI models trained in controlled environments deliver consistent results in real-world clinical practice? These challenges aren’t minor details, they’re critical factors that determine whether AI will actually benefit patients or exacerbate existing inequalities. AI is only as good as the data it’s trained on, and success in testing doesn’t always translate into clinical impact. To make the most of AI in healthcare, we need to go beyond the technology itself. Healthcare professionals don’t need to be AI experts but they do need the knowledge to recognize when and where AI can add value, interpret AI outputs accurately, and explain AI-driven processes and results to patients and colleagues. This requires investment not just in technology but in training, governance, and public trust. Without these foundational elements, even the most advanced AI tools risk being underutilized, or worse, misused. Prime Minister Starmer recently spoke about making the UK a leader in AI, promising to “unlock long-term investment” and “deliver for working people.” While this sounds promising, I’m left wondering: How will this vision address the realities of sectors like healthcare? AI undoubtedly has transformative potential, but unless it’s deployed responsibly, its benefits risk being unevenly distributed, and its flaws — like algorithmic bias — could deepen existing inequalities. The reality is that even the most sophisticated AI models can perpetuate disparities if not trained with diverse, representative data. If we want AI to truly deliver for healthcare, it must go beyond buzzwords and ensure ethical implementation, robust oversight, and meaningful outcomes for patients. We must also be mindful of how AI’s flaws can be mitigated. Strategies for detecting and addressing bias, such as developing diverse datasets, implementing fairness-aware algorithms, and ensuring transparency in AI decision-making, are pivotal for creating AI technology that is generalizable, fair, and truly beneficial in healthcare. To those working at the intersection of AI and healthcare: How do we ensure that AI delivers real, measurable progress? How do we strike the right balance between innovation and practicality, between hype and reality?

AI integration is the key to secure, efficient innovation in the healthcare sector. AI-powered tools are more than just a technological advancement. Rather, they promise to improve patient care by handling administrative burdens– such as scheduling and billing– allowing practitioners to spend more time treating people in need. This can lead to significant cost savings for healthcare institutions. Patients deserve attentive, individualized care, and practitioners deserve the time and resources to give it to them. AI can ensure better outcomes for practitioners and patients alike. Infuzu is at the forefront of AI development in the healthcare sector, and is committed to help providers navigate this new frontier responsibly. Infuzu is proud to offer HIPAA-compliant solutions that ensure privacy and security are never compromised for the sake of convenience. One of the strengths of using AI tools in the healthcare sector is their ability to quickly and accurately analyze data. AI algorithms excel at recognizing patterns, even within larger, complex data sets. AI technology’s strength and security can result in a better understanding of disease patterns and patient behaviors without compromising on privacy. While concerns remain about the accuracy of AI algorithms, AI technology is constantly evolving and improving. AI tools are a positive feedback loop– the more data they analyze, the more accurate they become. Additionally, they should be used in conjunction with human expertise, not as a replacement. When used responsibly, the benefits of AI in healthcare far outweigh any downsides. Infuzue is dedicated to upholding the highest ethical standard possible, and is excited for the future of AI in healthcare.