Sessions/Tracks

The rising cost of healthcare delivery has accelerated the need for AI-based optimization. AI-driven analytics enable hospitals and payers to identify inefficiencies, streamline resource allocation, and reduce unnecessary interventions. From automating repetitive administrative tasks to predicting supply chain demands, AI ensures smarter utilization of limited budgets. Machine learning models can also forecast patient outcomes and guide value-based care, lowering readmissions and improving reimbursement strategies. With global healthcare systems under financial strain, AI emerges as a powerful enabler of affordability, equity, and operational resilience.

• AI-powered hospital cost reduction models
• Predictive analytics for resource and workforce planning
• Value-based care supported by machine learning

Tech giants are rapidly shaping the healthcare AI ecosystem by investing in infrastructure, data platforms, and scalable solutions. Companies like Google, Amazon, and Microsoft are leveraging their cloud and AI expertise to transform healthcare delivery, from predictive modeling to virtual assistants. While their entry accelerates innovation, it also raises questions around monopolization, ethics, and regulatory oversight. These firms are building collaborations with hospitals, pharma, and startups to bring precision healthcare closer to reality. Understanding the influence of big tech is essential to balance innovation with fair competition and patient data protection.

• Strategic collaborations between tech giants and healthcare systems
• Ethical considerations of big tech dominance in health data
• Cloud-based AI ecosystems for clinical applications

AI chatbots are increasingly used in mental health support, but their adoption introduces unique risks. While they offer scalable, 24/7 access to therapy-like interactions, poor design or unregulated models may lead to misinformation, unsafe advice, or emotional harm. Ensuring conversational AI aligns with clinical safety standards is critical to prevent worsening conditions in vulnerable users. This track examines both the promise and pitfalls of chatbot integration into mental health care, emphasizing human oversight, trust frameworks, and clinical validation. The future of mental health chatbots lies in balancing innovation with ethics and safety.

• Clinical validation of conversational agents in therapy
• AI safety frameworks for mental health tools
• Human-AI collaboration in psychiatric support

Generative AI in healthcare is moving beyond text generation to clinical-grade contextual awareness. These systems can assist in summarizing patient histories, drafting care plans, or generating insights tailored to specific conditions. Unlike general models, clinically aware generative AI integrates domain-specific knowledge, reducing the risks of hallucinations or irrelevant recommendations. By embedding real-time clinical guidelines and patient context, these tools act as decision-support systems for physicians. The challenge remains ensuring reliability, transparency, and regulatory compliance while tapping into their transformative potential.

• Generative AI for clinical documentation and summarization
• Reducing bias and hallucinations in medical generative models
• Domain-specific fine-tuning for specialty care applications

Healthcare data is highly sensitive, and federated learning offers a solution by enabling AI models to train across multiple institutions without direct data sharing. This approach enhances collaboration between hospitals, pharma, and research centers while keeping patient privacy intact. By distributing algorithms instead of raw data, federated learning builds robust models from diverse datasets, improving accuracy and reducing bias. It also ensures compliance with evolving privacy regulations like GDPR and HIPAA. As trust in data governance grows, federated learning is becoming a cornerstone of ethical AI innovation in healthcare.

• Federated learning frameworks for clinical collaboration
• Balancing accuracy and privacy in distributed AI models
• Real-world deployments in radiology and genomics

Regulatory Technology (RegTech) is transforming how healthcare organizations manage compliance in a rapidly evolving digital environment. With AI-driven monitoring, RegTech platforms can detect irregularities in real time, ensuring adherence to clinical, ethical, and legal standards. These solutions simplify reporting, automate compliance workflows, and reduce human error, making regulatory processes more efficient. For industries navigating FDA, EMA, or HIPAA guidelines, RegTech ensures smoother audits and faster innovation cycles. Ultimately, it creates a transparent environment where innovation thrives without compromising safety.

• AI-driven compliance monitoring in clinical trials
• Automating regulatory reporting with intelligent systems
• Global harmonization of digital health regulations

Electronic Health Records (EHRs) are evolving from static storage systems into dynamic, cloud-native platforms enhanced by AI. These intelligent EHRs can auto-populate clinical notes, suggest diagnostic codes, and offer predictive decision support. By leveraging cloud infrastructure, they ensure seamless data accessibility across care settings while maintaining security. AI integration also reduces clinician burnout by streamlining workflows and minimizing administrative overhead. With interoperability at the core, cloud-native EHRs are creating a connected healthcare ecosystem where data drives proactive care.

• AI-driven clinical decision support within EHRs
• Interoperability standards for global healthcare exchange
• Reducing physician burnout with intelligent documentation tools

Hospitals are complex ecosystems, and predictive analytics is becoming indispensable for managing them efficiently. AI models can forecast patient admissions, optimize bed utilization, and anticipate ICU demand. Predictive insights also aid in staffing schedules, reducing wait times, and preventing supply shortages. By analyzing historical and real-time data, hospitals can achieve operational excellence while maintaining quality care. With financial pressures mounting, predictive analytics empowers administrators to balance efficiency and patient safety.

• Demand forecasting for emergency and ICU services
• Predictive supply chain and resource allocation models
• Workforce optimization using AI-driven scheduling

Digital therapeutics are emerging as evidence-based, software-driven treatments prescribed alongside or in place of conventional therapies. From managing chronic illnesses to supporting mental health, DTx solutions are expanding the definition of medicine. Their clinical validation ensures measurable outcomes, distinguishing them from wellness apps. By integrating into healthcare systems, they enable physicians to personalize treatment plans and monitor adherence remotely. The rise of DTx marks a paradigm shift where software is as vital as pharmaceuticals in patient care.

• Clinical validation and FDA approvals for DTx
• Integration of digital therapeutics into care pathways
• Scaling adoption across healthcare ecosystems

Artificial intelligence is enabling earlier and more accurate detection of mental health conditions by analyzing speech patterns, facial expressions, and digital behaviors. These tools complement clinical evaluation by offering objective, data-driven insights. AI models can detect subtle changes in mood or cognition, supporting timely interventions. While the promise is significant, ethical safeguards are essential to protect privacy and prevent misuse. With validated tools, AI-powered diagnostics could transform mental health from reactive to proactive care.

• Machine learning in depression and anxiety detection
• Multimodal biomarkers for psychiatric conditions
• Ethical oversight in AI-driven behavioral monitoring

Trust is the foundation of AI in healthcare. For widespread adoption, systems must be ethical, explainable, and free from bias. Transparent AI ensures clinicians and patients understand how decisions are made, while ethical governance prevents misuse. By addressing fairness, accountability, and inclusivity, healthcare AI can align with societal values. This track emphasizes frameworks and policies that ensure AI improves care without compromising trust.

• Bias mitigation in healthcare AI datasets
• Explainable AI for clinical decision-making
• Ethical governance frameworks for digital health

Conversational and agentic AI is moving beyond chatbots into advanced workflow integration. These systems assist clinicians by handling patient intake, triaging symptoms, and managing follow-ups. Agentic AI can act autonomously within set boundaries, scheduling appointments, ordering labs, or flagging urgent cases. By reducing administrative burdens, these technologies allow healthcare professionals to focus on patient care. Their role is expanding as natural language understanding improves, making interactions smoother and more human-like.

• Autonomous agents in patient flow management
• Natural language processing for clinical documentation
• Conversational AI for patient engagement and education

Remote patient monitoring technologies are extending the reach of healthcare into homes and communities. Devices powered by AI track vitals, detect early warning signs, and alert clinicians to potential complications. RPM is especially valuable for chronic disease management, post-operative recovery, and elder care. With 5G connectivity and wearable innovations, monitoring has become continuous, personalized, and predictive. This track explores how RPM is reshaping the doctor–patient relationship toward proactive care models.

• AI-enabled wearables for chronic disease monitoring
• Integration of RPM into hospital and insurance systems
• Enhancing patient adherence through real-time feedback

Immersive technologies like AR, VR, and the metaverse are opening new dimensions in healthcare training, therapy, and access. Medical students can practice in virtual operating rooms, while patients benefit from VR-based pain management or exposure therapy. The metaverse also provides inclusive platforms for global collaboration and remote care delivery. By breaking geographical barriers, these tools democratize healthcare access and enhance medical education. This track highlights how immersive technologies are moving from novelty to necessity in modern medicine.

• VR-based therapies for pain and mental health
• AR in surgical planning and medical training
• Virtual clinics within the metaverse for global patients

Digital twins create a virtual replica of a patient’s physiology, enabling simulations of treatment responses before they are applied in real life. By combining genomics, imaging, and lifestyle data, digital twins pave the way for highly personalized interventions. Clinicians can predict how a patient will respond to drugs or surgeries, reducing trial-and-error approaches. As computational models become more precise, digital twins hold promise for oncology, cardiology, and chronic disease management. They represent the next frontier in individualized care.

• Patient-specific digital twin models in cardiology
• Simulation of drug responses in oncology
• Integration of digital twins with clinical decision support

Administrative complexity consumes significant healthcare resources, and automation is emerging as a powerful solution. AI-driven tools automate billing, claims processing, appointment scheduling, and compliance reporting. By reducing paperwork and errors, automation frees healthcare staff to focus on patient interaction and care quality. It also shortens reimbursement cycles and improves financial transparency. In a resource-constrained environment, administrative automation is not just efficiency—it is survival.

• Robotic process automation in healthcare finance
• Intelligent scheduling systems for outpatient services
• AI-driven claims management and fraud detection

With healthcare data increasingly digital, cybersecurity is critical. AI is playing a dual role—both as a defense mechanism and as a tool attackers may exploit. Machine learning models detect unusual patterns, predict vulnerabilities, and respond to threats faster than traditional systems. Protecting patient data, clinical workflows, and connected medical devices is paramount to trust in digital health. This track explores how AI enhances resilience against ransomware, phishing, and cyberattacks.

• AI-enabled anomaly detection in healthcare IT systems
• Securing medical IoT and connected devices
• Building resilient cybersecurity frameworks with AI

AI-powered health coaching is bringing personalized wellness guidance directly to consumers. From smart wearables to virtual nutritionists, AI tailors exercise, diet, and lifestyle plans based on real-time data. These tools bridge preventive health with daily living, empowering individuals to manage their well-being proactively. As wellness converges with clinical care, consumer-focused AI could reduce disease risk and encourage healthier communities. This track examines the role of AI in democratizing personal health.

• Personalized wellness algorithms in wearables
• Virtual health coaches for nutrition and fitness
• Consumer wellness apps integrating with healthcare providers

Brain–computer interfaces (BCIs) and neurotechnologies promise revolutionary treatments for paralysis, epilepsy, and mental illness, but they raise profound ethical questions. Issues of autonomy, consent, privacy, and identity come into focus when devices directly interface with the brain. This track explores how to balance innovation with human rights, ensuring that advances serve patients without exploitation. With BCIs rapidly advancing from labs to clinics, the dialogue around ethical safeguards is more urgent than ever.

• Neuroethics of invasive vs. non-invasive BCIs
• Privacy and security in neural data collection
• Regulatory frameworks for emerging neurotechnologies