Can Artificial Intelligence Help Prevent Life-Changing Complications from Poorly Fitted Medical Devices?
When a person receives a medical or assistive device—such as therapeutic footwear, foot orthotics or ankle-foot orthoses—the prescription is often based primarily on clinical assessment and practitioner experience. Yet every patient has unique needs, preferences, lifestyles and daily activities. What works well for one person may be uncomfortable, impractical or ineffective for another. As a result, patients may not consistently use prescribed devices, reducing their effectiveness and potentially leading to poorer health outcomes. In some cases, particularly for people at risk of diabetic foot complications, these outcomes can be severe, including ulceration, disability and even lower-limb amputation.
This challenge is at the heart of a National Industry PhD project led by Prof. Ashad Kabir from Charles Sturt University in partnership with Foot Balance Technology Pty Ltd (FBTech), and being undertaken by PhD candidate Kunal Kumar. Kunal is supervised by Prof Ashad Kabir as principal supervisor, Dr Luke Donnan as co-supervisor at Charles Sturt University, and Dr Sayed Ahmed as industry co-supervisor at FBTech. The project is exploring how artificial intelligence (AI) and machine learning can support the prescription of personalised medical and assistive devices, moving beyond one-size-fits-all approaches and towards recommendations tailored to each individual's circumstances. As Prof. Kabir explains, “Our goal is not to replace clinical expertise with AI, but to strengthen it. By combining real-world clinical data with artificial intelligence, we aim to help clinicians make more personalised, evidence-based prescription decisions that better reflect each patient’s needs, lifestyle and circumstances.”
The research draws on a substantial clinical resource: approximately 3,000 historical patient records held by FBTech, containing information about patient preferences, prescribed devices, adherence and clinical outcomes. By analysing these data, the research aims to identify patterns that can help clinicians make more informed and evidence-based decisions when prescribing assistive devices. The ultimate goal is an AI-guided decision-support system capable of recommending personalised prescription plans that better align with individual patient needs and lifestyles.
Importantly, the project is not simply about building an algorithm. The research is also investigating the human factors that influence successful outcomes. Early work has included observing clinical consultations and examining real-world prescription processes within FBTech clinics. These activities have helped build a detailed understanding of how clinical decisions are currently made and the many variables that influence patient adherence and outcomes.
Beyond the potential benefits for patients and healthcare providers, the project is also delivering significant value for the people and organisations involved in the research itself. For PhD candidate Kunal Kumar, the project has provided a rare opportunity to work at the intersection of artificial intelligence, healthcare and industry. As Kunal explains, “The NIPHD project has been a defining part of my PhD journey, giving me the opportunity to work at the intersection of academia and industry. It has broadened my perspective on how research and industry can work together to address real-world challenges and create meaningful impact.” Through regular visits to Foot Balance Technology clinics, observation of real-world consultations, participation in industry events and direct engagement with practitioners, Kunal has developed a deep understanding of the footwear prescription process and the practical challenges clinicians face every day. The project has also supported his development as a researcher, contributing to multiple publications, conference participation and the acquisition of specialised skills in machine learning, health data analysis and research translation.
For industry partner Foot Balance Technology, the research is generating new knowledge that can help strengthen its evidence-based approach to personalised care while exploring new opportunities for innovation. By contributing clinical expertise, supporting participant recruitment and providing access to real-world datasets, the company is helping shape a technology designed to address a genuine industry challenge. The long-term potential extends beyond this single project. The research is expected to support the development of AI-enabled prescription tools that could improve consistency in clinical decision-making, reduce reliance on individual practitioner experience and create new opportunities for commercialisation, partnerships and wider adoption of personalised device prescription technologies across the healthcare sector.
The potential beneficiaries of this research extend well beyond a single organisation. For patients, a more personalised prescription process could improve adherence, satisfaction and clinical outcomes by ensuring devices better match their daily realities and preferences. For clinicians, the system could provide evidence-based support that reduces variability in practice and assists decision-making. For healthcare providers, more effective prescriptions may contribute to improved efficiency and reduced costs associated with poor adherence and suboptimal treatment outcomes.
The project is also demonstrating the value of industry-university collaboration. FBTech has played an active role throughout the research, reflecting the company ethos of providing accessible evidence-based care through individualised treatment plans designed to improve function, support independence and enhance quality of life through measurable clinical outcomes.
The collaboration is enabling academic research to be grounded in real-world clinical practice while creating opportunities for future innovation in digital health and assistive technologies.
While the final AI-guided prescription system remains under development, the project has already delivered important outcomes: multiple research publications, validated datasets, consensus-based clinical knowledge and an emerging framework for transforming clinical expertise into scalable decision-support tools. As healthcare increasingly embraces data-driven approaches, this research offers a compelling example of how AI can be applied not to replace clinicians, but to augment their expertise and help deliver more personalised, effective care.