Artificial Intelligence in healthcare and research

Chair: Nathan Lea

Abstract

This session aims to highlight and discuss the implications and fast changing developments across AI technology and regulations:

• For healthcare practitioners using assistive and autonomous AI to deliver care and the role of practitioner autonomy and decision-making
• For patient access to quality care and the autonomy, and the rising anxiety against data bias
• For researchers to understand regulatory position to reuse data and the risks of working with data analytics, where data bias may exist, and incomplete data may result in AI skewing results.

The plenary session will explore and discuss the regulatory and ethical implications of applying AI in health care delivery and across clinical research. This will cover topics such as AI’s impact, opportunities and risks on the quality of healthcare with assistive and autonomous AI devices on both patient and practitioner’s autonomy and decision making, and for data access, quality and scale using AI algorithms for clinical research and trials.

AI has already started demonstrating huge capabilities in detecting and diagnosing diseases, through enhanced algorithms able to analyse and interpret vast amounts of data sets, medical images, and patient symptoms. This makes it possible to identify trends, potential diseases, and predict their progression. For healthcare providers, this can lead to more accuracy in diagnosis and a future of personalised treatments. For patients, this can improve outcomes, especially for patients in more rural and isolated communities where assisted and autonomous technologies can be vital due to lack of healthcare infrastructure and personnel. For researchers, AI adoption can make it possible to reduce sample sizes, decentralise trials, enhance participant recruitment, reduce costs, and offer more customised and adaptive trials and studies including the use of wearable devices, decision support and recommendations.  The impact can already be felt in specialised areas of research with low ROI such as rare diseases, where efficiency in recruitment and trial design, enhanced data and text analysis power can exponentially improve trial success.

Increasingly, regulators, healthcare providers and patient advocacy groups are questioning wholesale adoption without ethical oversight and a better understanding of forthcoming regulations including the AI Act. The challenge today is for DPOs to navigate this changing landscape and ensure that regulatory requirements and the reasonable data protection expectations of their Data Subjects and organisations are met.

In this session we pose the questions:

• What are the implications of AI powered tools for human decision making and for the autonomy of healthcare providers, patients and researchers?
• How do we clarify the legal position and ensure consistency and quality of data use and re-use?
• How will existing and forthcoming regulatory requirements and the AI Act address these concerns, and what can DPOs do to align data protection requirements with ethical principles, and codes of conduct across all stakeholders?

Structure

These two 90 -minute sessions will begin with three 15-minute presentations

This will be followed by a 40-minute panel discussion between them on the challenges that the Regulation will place on different healthcare and research stakeholders, and what practical measures might best enable EHDS success. Time will be included for audience questions and interaction. The session will close with a final summing up of key considerations that policy makers should take on board.