Sessions

Successful digitalization requires bold and courageous leadership – with extensive skills in change management and fully understanding Return on Investment (ROI) – “bang for the buck”. This lecture will address the challenges to all healthcare leaders in digitalization, with the objective that all decision making must be based on facts to ensure that a change always is an improvement.

Artificial intelligence (AI) has been used to support decision making in the mental health domain for both healthcare professionals and patients on different levels in the healthcare systems and self-care services. AI has been included in several health applications such as predicting mental health problems, diagnostics, treatment plans, workflows, and other duties that require decision support. Understanding the use of AI tools and how it is applied for support decision making in the mental health would contribute in improving the design, development and implementation in practice. Further knowledge will also help benefit shaping person-centered care approaches when using AI tools by understanding the dynamics used nowadays in support decision making and how to include patients in the development of future processes and tools,It is unclear what types of decisions that AI tools most commonly participate in supporting in mental health, how these tools are applied, and whether its implementation is engaging patients in the process of decision making or not, this uncertainty may contribute in shifting the decision- making process towards more technology polarization and far from patient centeredness. Therefore, a scoping review is conducted to map the characteristics of research done on AI tools for supporting decision making in the mental health field to contribute to the body of knowledge regarding how decisions are supported. In the proposed lecture we will present our findings regarding the use of AI in mental health and reflections on support decision making.

Artificial intelligence (AI) has great potential to transform healthcare, making it more efficient, equitable, and safe. There are hundreds of AI-based tools available on the market today, yet few get adopted into clinical practice because of numerous barriers and challenges. This talk provides an overview of these hurdles and suggests 26 considerations synthesized from the literature, interviews, and workshop with stakeholders who have experience with the process of trying to adopt AI-based tools into clinical practice. We believe that as the barriers and challenges to AI adoption are addressed and overcome, AI will inevitably have a lasting impact on improving healthcare for all.

Since 2021, the Swedish Medical Products Agency has been building AI competence and capacity to meet regulatory needs and enable intelligent automation. In this session we present both in-house development projects and regularory activities within the EMA network.

In this seminar, Michelle will give an overview of human-centered Artificial Intelligence at AstraZeneca's Digital Health Research & Development.

A lecture that is 100% generated and held by an AI? A few years ago that might have sounded like a science fiction scenario, but with the current tools such as Replika, GPT-3, DALL-E, MidJourney and more, AI content is being produced in a very high speed. In this lecture, a combination of AI techniques will be used to generate a lecture, fully held by the AI avatar ANDI about what the various algorithms think about the advancement of AI in health care for diagnosis, treatment and support. 

Why is AI proving more of a challenge in Healthcare than other industries?  AI has been promoted as the saviour of medicine yet has failed to make the difference many predicted. Here in this presentation we will explore how AI has changed the potential for healthcare and the barriers that are still faced.

With the fast progression in AI, its role in healthcare is bound to be ubiquitous in near future. However, creating AI software that is unbiased and trained ethically remains a challenge. In this session, we will evaluate different aspects of what it means for the AI to be biased and how we can evaluate it as a medical device software. Similarly, it is important to understand the fact that ethical AI is a two-way street. We will also look into how the society and the regulatory bodies should play a role for AI to be constructed in an ethical way. 

Healthcare longitudinal data offers endless opportunities such as diagnostics, and prognostics, to care personalization and improving healthcare quality and efficiency. These can be achieved through Artificial Intelligence (AI), data science, and in specific machine learning (ML). However, lack of inscrutability of the AI/ML-based techniques has hampered pervasive AI in healthcare systems. In this talk, we will introduce graph-based approaches for collecting, storing, visualizing, analyzing, and modeling healthcare data, and discuss how they outperform the other available approaches. We will also discuss challenges around health data and how synthetic data can mitigate them.