A multidisciplinary team and multiagency approach for AI implementation: A commentary for medical imaging and radiotherapy key stakeholders
Nikolaos Stogiannos, Caitlin Gillan, Helle Precht, Claudia sa dos Reis, Amrita Kumar, Tracy O'Regan, Vanessa Ellis, Anna Barnes, Richard Meades, Michael Pogose, Julien Greggio, Erica Scurr, Shamie Kumar, Graham King, David Rosewarne, Catherine Jones, Kicky G. van Leeuwen, Emma Hyde, Charlotte Beardmore, Juan Gutierrez Alliende, Samar El-Farra, Stamatia Papathanasiou, Jan Beger, Jonathan Nash, Peter van Ooijen, Christiane Zelenyanszki, Barbara Koch, Keith Antony Langmack, Richard Tucker, Vicky Goh, Tom Turmezei, Gerald Lip, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso, Geraldine Dean, Shashivadan P. Hirani, Sofia Torre, Theophilus N. Akudjedu, Benard Ohene-Botwe, Ricardo Khine, Chris O'Sullivan, Yiannis Kyratsis, Mark McEntee, Peter Wheatstone, Yvonne Thackray, James Cairns, Derek Jerome, Andrew Scarsbrook, Christina Malamateniou (see publication in Journal )Abstract
Artificial Intelligence (AI) algorithms have been recently deployed in different healthcare settings. These tools have shown promise in reducing professionals’ administrative workload, handle electronic health records, aid drug discovery, improve diagnostic services, and analyse complex data [1], [2], [3]. Medical Imaging and Radiotherapy (MIRT) are at the forefront of this digital transformation, as can be seen from the concurrent increase in MIRT-related AI products [4], [5], [6]. In MIRT in particular, AI has shown potential to optimise image acquisition and post-processing, redefine clinical workflows, improve diagnostic accuracy, facilitate automated organ segmentation, image registration and planning in radiotherapy, and personalise patient care [7], [8], [9]. These recent advancements could translate into improved patient outcomes, personalised pathways and treatment plans, and, therefore, deliver precision into healthcare [10,11]. At the same time, concerns about potential risks and safety of AI-enabled software and hardware are raised by both clinical practitioners and patients, which need to be addressed within the design and implementation of AI and balanced against the undoubted benefits [12].
Being at the front of technological advancements comes at a cost. As AI applications emerge, are tested and rigorously evaluated in clinical places, MIRT professionals are responsible for the fine-tuning, co-ordination, and governance of the clinical implementation of AI. While various professions, agencies and stakeholders in related disciplines work to understand what impact and change AI implementation might have on clinical workflows, their future roles and careers, and to address local challenges in knowledge gaps, training, and the wider workforce, interprofessional coordination is of paramount importance [13].