“The Basics” Bring everyone to a similar playing field. For anyone new to AI in healthcare, or has basic questions

What is Artificial Intelligence, and how is it used in Healthcare?
What is AI, Machine Learning, Deep Learning
What is Generative AI? Strengths, Weaknesses, and Risks
What is Prompt Engineering, and how do i get started with GenAI
AI Beyond Text: Generating images, videos, sound, actions, & other sequences with AI.

This session will explore how AI is addressing the high demands of modern healthcare by improving diagnostic accuracy, reducing errors caused by human fatigue, and automating repetitive tasks to enhance efficiency. AI-driven solutions are transforming various fields, including cancer research, dermatology, and primary care, where tools like AI scribes and Microsoft Co-Pilot are streamlining workflows. By reducing costs and mortality rates, AI has the potential to revolutionise patient care, and this session will highlight its real-world applications and future possibilities.

Critical issues of bias in AI, exploring its causes, mitigation strategies, lack of clinician education& real healthcare examples, examines liability concerns around AI errors, not enough evidence/ data, pumping out new technology without solving issues at hand. Concludes with a panel discussion on the ethical considerations of AI in healthcare and room for questions. Problem: AI in healthcare faces critical challenges, including bias in algorithms due to incomplete or unrepresentative datasets, a lack of clinician education on AI integration, and the rapid development of new technology without adequately addressing existing issues. Additionally, concerns around liability for AI-driven errors and the insufficient evidence base for AI applications further hinder trust and adoption in clinical practice.
Solution: To mitigate these challenges, AI models must be trained on diverse, high-quality datasets to reduce bias, and comprehensive education programs should be implemented to equip clinicians with the knowledge to effectively use AI tools. Robust validation frameworks and regulatory oversight are needed to ensure AI solutions address real healthcare problems rather than introducing new risks.
Implementation: This requires collaboration between AI developers, healthcare institutions, and policymakers to establish ethical guidelines, standardised validation protocols, and legal frameworks for liability concerns. Encouraging real-world case studies and evidence-based research will strengthen AI’s role in healthcare. The session will conclude with a panel discussion on the ethical implications of AI in healthcare, allowing for an open Q&A to explore future directions and necessary safeguards.

This session explores the real-world impact of AI in healthcare from the patient’s perspective, highlighting both the benefits and challenges they face. Through personal stories and lived experiences, patients will share how AI-driven tools have influenced their diagnosis, treatment, and overall care. The session will also address concerns such as trust, transparency, and ethical considerations, offering valuable insights into how AI can be developed and implemented to better meet patient needs.

An opportunity to learn more about the field of health tech, how to get into the field and a chance to ask Dr keith Grimes any questions regarding his journey into the field, from being a GP to a digital health and innovation consultant.

From Stethoscope to Startup: How Doctors Can Break Into Tech

In this workshop led by Dr. Keith Grimes, participants will explore the exciting intersection of healthcare and technology. Designed for medical professionals curious about transitioning into the tech industry, this session will provide actionable insights on leveraging clinical experience to thrive in digital health, health tech startups, and beyond. Attendees will gain practical advice on identifying opportunities, building relevant skills, and navigating the shift from traditional healthcare to the fast-paced world of tech innovation. Whether you're interested in founding a startup, working in healthtech product development, or advising on medical innovations, this workshop will equip you with the knowledge and confidence to make the leap.

A discussion on the lack of AI and technology available for the treatment of paediatric health. The importance of paediatric health and what could be possibly done to improve healthcare outcomes.
Problem: Despite rapid advancements in AI-driven diagnostics and treatment, paediatric healthcare remains underrepresented in technological development. This gap limits early diagnosis, personalised treatment, and accessibility of care, with challenges such as ethical concerns, limited paediatric-specific data, and a lack of tailored AI solutions.
Solution: To bridge this gap, there is a need for AI-driven innovations specifically designed for paediatrics, including improved data collection, age-specific algorithms, and interdisciplinary collaboration between paediatricians, AI researchers, and healthcare technologists.
Implementation: Integrating AI into paediatric treatment pathways requires investment in research, the development of ethically sound AI models, and collaboration between healthcare institutions, policymakers, and technology developers. Encouraging real-world applications through pilot programs and clinical trials can ensure AI is effectively and safely utilised in paediatric care.

Having explored AI’s current applications, this session will look ahead to its future influence in healthcare. From known advancements to unreleased innovations, we will delve into upcoming AI-driven technologies, their potential impact, and what healthcare professionals should watch for. Attendees will gain insight into emerging opportunities and how they can actively engage with and contribute to the evolving AI landscape in medicine.