The Future of Healthcare Technology: What Clinicians Need to Know

Good morning, good afternoon, good evening everyone. Thank you so much for joining us for this wonderful conversation that Miltos and I are going to have today. Thank you for sharing an hour of your time, and I want to thank Osmosis for hosting this important conversation. We are thrilled to be here and to share our curiosity and passion about healthcare technology and the intersection of those wonderful domains. As you all are putting in where you are from, where you are located, I am going to introduce myself. My name is Dipu Patel. I am a physician assistant; I have been a PA for 25 years. My clinical background is in emergency medicine, urgent care, and hematology oncology. I have been in academia for about a decade, maybe a little bit over. I am currently Vice Chair for Innovation and Professor at the University of Pittsburgh, but I come to you by way of Boston, where I am currently residing. My passion lies in digital health, AI, and quality improvement science. I have learned so much over the last several years about what technology can do to help us leverage the quality of care that we deliver. I am going to hand it off to Miltos to introduce himself and then we will dive into some questions. Thank you so much. I am so happy today to meet the wonderful community of Osmosis, and I want to publicly congratulate them for this wonderful service that brings the latest knowledge to our communities. My name is Miltos. I am a computer scientist with a special interest in digital transformation in the healthcare domain. I have been an academic for the last 25 years and have offered consultancy in the Middle East, including Saudi Arabia. I have worked for the Saudi Commission for Health Specialties and the Saudi National Institute of Health, running several digital transformation projects. We are all interested in how AI can add value to our societies. I also have a special interest in motivating young people to do research, especially those from other domains. I think multidisciplinary research is the way forward and also helps deal with the hype that exists today. I am really happy to meet you today and thank you so much. Thank you, Miltos. No matter whether you are a student early in your career, a seasoned clinician, or somewhere in between, this conversation is not about coding or the nitty-gritty of technology. It is about people and the care you deliver. It is not just about computational intelligence but about clinical intuition, still leveraging the human aspects of technology that allow us to do what we do best. Although tools will shift in the near future, the human element will remain at the core of everything we do as clinicians. Without further ado, I will kick off our conversation. Please feel free to put questions in the chat. We will do our best to answer them over the next hour. We will start by looking forward: What will AI in medical practice look like 10 years from now? What should clinicians and caregivers look for as they learn or as seasoned learners? As someone who came to digital health and AI later in my career, it has made me really optimistic about the future of care delivery. One nice thing about bringing Miltos and me together is that I have practiced in the US and have some understanding of the Indian subcontinent?s care and technology infrastructure. Miltos, as he mentioned, covers the European subcontinent and the Middle East. So we have a broad swath of experience. Technology knows no boundaries; it is about regulation and application in various areas. It can leverage cultural nuances in care practice. Miltos, what do you think medical practice will look like with AI in 10 years? Thank you. First, we must understand the scale of AI application. At the individual level, you need to update your skills and knowledge. Services like Osmosis deliver great resources. At the top level, in executive administration and government decision-making, AI must orchestrate different levels. For example, in Saudi Arabia and the Middle East, digital transformation is a top priority. AI and emerging technologies enable daily healthcare delivery. I see augmented intelligence as bringing value to practitioners, helping them be more effective in daily challenges. Computational AI must improve in accuracy?we cannot accept mistakes in healthcare. Ethical issues will arise, and it is unclear who will be responsible. Data management is critical. Patients might hesitate to have their data used by AI for personalized medicine. We must work at consultation and policy-making levels. I completely agree. We are in early adoption stages. Much research is needed to prove AI is safe and explainable. We have many gaps to fill before full-scale use as envisioned. However, the human element?us healthcare providers?will drive change. How and where we implement AI is up to us. AI will be almost invisible in clinical workflows in the next 10 years or less. Humans will remain ?in the loop.? Clinical judgment will not be outsourced but enhanced by AI tools. Clinicians and learners are lifelong learners. We need to develop AI literacy to understand algorithm development and clinical application to the right patient at the right time. Clinical intuition comes from experience. AI can assist less experienced clinicians with reasoning. Some may fear AI?s impact on practice, but it is another tool?like a stethoscope with AI assistance. We need research and vetting to apply AI safely to improve patient outcomes. Miltos, anything to add? I agree. Academia and industry are vital. Many brilliant AI startups add value to the AI health ecosystem, providing trusted, ethical AI for clinical use. Developing a pilot or prototype is different from commercializing AI services for hospitals. We must integrate healthcare and pharma AI efforts for personalized medicine. I fully agree. I will define healthcare technology broadly?not just IT help desks, but full-scale technologies like EHRs, AI algorithms, telehealth, remote patient monitoring, glucose monitors, smartwatches, and more. We have vast data but need to know how to apply it to improve care. A question from our audience: Which EMR/EHR is best? Is it Epic? Miltos, do you have experience with Epic? Yes. We do not promote specific commercial solutions, but we are in a transition era. Platforms like Epic will soon be enhanced with sophisticated services for healthcare practitioners. The human part remains critical. We must balance technology adoption with human well-being. Burnout and workload are concerns; adding AI complexity requires thoughtful administration. I agree. Many current systems are legacy infrastructures not easily adapted to AI. The best system is one that augments clinician-patient relationships without cognitive overload. Some systems are small and nimble but lack ecosystem-wide integration. Finding the right fit is key. A question from Zagi Zatab: What if an AI-powered stethoscope makes mistakes? Who pays the consequences? Miltos? AI regulation is key. No higher administration will allow errors without clear procedures and support mechanisms. Humans will minimize error possibilities. Human loss must be avoided. I agree. The human remains responsible. ?Human in the loop? is essential. Regulatory landscapes will evolve with practice styles. I do not foresee AI making clinical decisions without human oversight in my lifetime. AI is not ?thinking? like humans; it processes data and patterns. Clinical decisions remain human responsibilities. Many FDA-cleared AI algorithms have human oversight. For example, radiology AI assists but radiologists confirm diagnoses. There are pros and cons, but clinicians remain responsible. Next question: What effects will AI have on medical research and quality? Miltos? AI?s impact will be mixed. We are still working on metrics and regulations. Research quality should improve as AI contributes to final outputs. For drug development, AI accelerates molecule redesign and repurposing. AI helps clinical trial recruitment by targeting patients via EMR data. This is just a glimpse of AI?s potential in research and pharma. Miltos? AI offers supercomputing capabilities for robust personalized medicine research. Large datasets require resources beyond individual researchers. AI tools help junior researchers draft reviews efficiently. Multidisciplinary research?healthcare, biology, computer science?is vital. Integration of pharma and healthcare AI is essential for personalized medicine. I agree. AI helps students expedite research but critical thinking remains vital. AI democratizes knowledge access, but brains still apply knowledge. AI is a tool to direct and focus learning, not to make it ?easier.? Osmosis provides bite-sized, sticky information to maximize learning. Another question: Can AI be used for research or diagnostic aids like X-rays for osteoarthritis? Yes. AI can read X-rays and provide diagnoses. For example, a 2015 model diagnosed diabetic retinopathy better than specialists. Early diagnosis improves outcomes and lowers complications. Miltos? AI applications vary. Pilot studies with limited data can produce clinical guidelines. Commercializing AI tools requires significant resources and innovative algorithms. There is a gap between academic research and real-world applications. We seek well-being and quality of life improvements. Another question: How is AI transforming diagnostic radiology? What are risks? Risks are similar to any technology. AI is only as good as its data and user. There is no one-size-fits-all. Personalized medicine and digital twinning are growing areas. AI surfaces data enabling new decisions. Think of lifelong, portable EHRs connected securely?still a future goal. Major risks include cybersecurity and hacking. We can mitigate risks as technology advances. Miltos? From computer science and healthcare perspectives: Descriptive analytics show current patient states. Predictive models help prioritize risk. Prescriptive analytics find best solutions, like optimal therapies. AI can optimize workflows and low-level tasks. Reducing inefficiencies helps healthcare delivery. A funny example: medical students spend too much time documenting. AI can help reduce such burdens. Imagine never entering lab orders manually again. Next question: How should medical professionals upskill in AI? AI literacy is essential?understand what AI can and cannot do. Technology is evolving rapidly?for example, ChatGPT in 2022 is far from today?s version. Learn prompt engineering and leverage AI to enhance learning. Faculty should embed AI literacy longitudinally in curricula. Even if not tech experts, graduates should know what questions to ask about AI tools. Miltos? Knowledge dissemination is vital. Osmosis contributes greatly. Academia is slow to develop AI-related degrees or certifications. Personalized, flexible learning paths are key. AI tools can help identify trusted content quickly. Real-world case studies enhance learning. Dipu? Agree completely. Medicine is interprofessional; future teams will include data scientists and engineers. Collaborating with these experts enhances patient care and decision-making. We must welcome new team members with unique skills. One more question: Advice for clinicians returning after 10 or 20 years away? Miltos? AI solutions can help update knowledge quickly. Instead of spending hours in books, get trusted knowledge in seconds. Explore AI startups providing solutions. AI can help professionals connect and communicate. Dipu? Stepping away does not mean you forgot medicine. Brush up on workflows and AI literacy. Pair with enthusiastic younger colleagues. Digital health champions at workplaces can help. AI evolves rapidly?you may never be fully ?up to speed.? Agentic AI is an upcoming trend we will need to learn about. Use lifelong learning skills to stay curious and engaged. Start small, tinker with large language models. Final question: Best medicine resources including AI? Dipu? Shout out to Osmosis and Elsevier resources?their partnership is fantastic. Free courses on Coursera, LinkedIn Learning, and universities are good for basics. Miltos? I recommend Elsevier?s books, such as ?Digital Transformation in Healthcare,? ?Artificial Intelligence and Big Data Analytics for Smart Healthcare,? and ?Innovation in Health Informatics.? Use critical thinking to find the best solutions?no one size fits all. Thank you, Osmosis, for hosting. I invite Osmosis to consider new AI-powered micro-credential services. Dipu and I are on LinkedIn?feel free to connect. I will wrap up. The future of medicine is not man or machine?it is man with machine, practicing art guided by data and powered by purpose. Humans remain the most important aspect of technology. Whether starting clinical training or returning after time away, approach learning with curiosity, compassion, and commitment. Technology will evolve, but human stewardship makes it meaningful. I firmly believe in technology with a human in the loop. Miltos? I agree. Let us honor our colleagues, respect each other, and create a supportive atmosphere. Technology today is AI, but in five years may be IoT, cloud computing, edge computing, blockchain, metaverse, and more. We must welcome new technologies and use human capability to guide them. I really enjoyed our conversation and wish we had more time. Thank you all for joining. We appreciate your time. Please let us know how we did and if you want more. Thank you, Miltos, and thank you to our audience. Helping current and future clinicians focus, learn, retain, and thrive. Learn more.