The Good: Chronic Wound (CW) healthcare management has well established care models in many countries that are fairly similar and effective for taking care of the patients and their wounds. With modern medicine, finding out the root cause, such as blood flow issues or diabetes, for the wounds is easy and accurate. The process of identifying, analysing, evaluating, creating a treatment plan and healing is a well structured process that rarely goes wrong.
The Bad: Taking care of CWs is time-consuming and expensive for clinicians as well as patients. As CWs take weeks to heal and do not heal like typical wounds, the patients need to have regular visits at the clinic and doctors or nurses need to take their time to help the patients. This uses a lot of time, money and effort even if the patient has a relatively short distance to the clinic, making it exponentially worse in rural areas where a patient has to travel longer distances. There is also no guarantee that the patient will receive the treatmen when they are scheduled as there might be more urgent patients or visits might take longer than expected.
The AI: The rise of Artificial Intelligence (AI) and Large Language Models (LLM) has changed the modern world in many ways. AI has enabled people to process data faster and find detailes that a human-eye might miss amongst thousands of data points. At the same time, AI helps with menial tasks allowing people to complete labour intensive tasks faster as long as the required output does not require too much human thinking. Similarly, AI helps with processing text and images in wide variety of situations. Within healthcare, AI can be used to go through patient medical records to summarise information as well as combine several data points together to find out possible underlying issues if the AI has been trained with pre-existing medical data.
While AI has been found to be helpful in many situations, it is not all sunshine and rainbows. There are cases where trained AI models have gone beyond their restrictions to do something unexcepted, such as hacking company systems or deleting databases. Additionally, when relying completely on AI without verification, the incorrect output will be unnoticed and may lead to larger issues. In healthcare systems, it is extremely important to verify any AI output if there is a risk of causing physical harm to others.
The future: AI and LLMs are here to stay regardless of what people think. How we use and perceive the AI is something we can change. Instead of having AI replacing humans, people should consider AI as a tool to enhance their work instead. In healthcare, computer systems have done image analysis for a long time and now with the help of AI, processing images is more accurate and previously unnoticed irregularities might be found out more easily. It is also important to consider that an AI model can go through data and images vastly faster than a human ever could, which can help clincians notice problems leading to faster care. But the AI model is there to support, and should not replace the knowledge and final say of an expert clinician.
In ICAREWOUNDS, the AI-powered system is meant to help clincians to help patients faster and more accurately, provide initial treatment plans that the clinician can oversee and verify. The AI will help clinicians with the menial tasks and enable patients to shorten their visits at the clinic and in the future, reduce the number of unneeded visits. ICAREWOUNDS aims to support, and not replace, clinicians with an AI-powered tool to ease taking care of chronic wounds and still keep the human touch and interaction between clinicians and patients, not reduce the interaction to just numbers on screen.
Learn more about the project:
https://icarewounds.eu/
ICAREWOUNDS project has received funding from the European Commission, ISCIII, NCBR, HRB, AKA and MUR under the framework the co-fund partnership of Transforming Health and Care Systems, THCS, (GA N° 101095654 of the EU Horizon Europe Research and Innovation Programme)
Award nº AC23_2/00048 and nº AC23_2/00031 by Instituto de Salud Carlos III (ISCIII) through AES 2023 and within the THCS Partnership.
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