AI Logic Of Care: Design for future medical care and premises for upgrading smart bandages for diabetic chronic wounds
Bibliographic record
Abstract
Prototypes occupy an important place in the development of new products and are a well-established form of investigation in today’s design practices. This paper focuses on helping future designers in the development process of Smart Bandages (SBs) prototyping for treatment and monitoring of diabetic chronic wounds (DCWs). Adopting a perspective of design as an intermediary between different stakeholders and embracing an interdisciplinary approach, this investigation demonstrates the complexity of innovation aspects in medical contexts and the need for new insights and conceptual frameworks to support medical practice. The study introduces the concept of “AI logic of care” to advance an approach that combines Annemarie Mol’s “logic of care” and AI tools prospects. The investigation brings into discussion George Canguilhem’s work and supports a medical rationality that encourages experimentation, but also advises carefulness and modesty in the decision-making process. Long-term observation, experience and experimentation are key concepts encountered also in Mol’s “logic of care” perspective. In both cases, it is about innovation; it is about the relationship between technology and care. As an emerging, inclusive, and interdisciplinary approach to prototyping, the “AI logic of care”, which acknowledges the lack of a “care-centred design” in the research related to smart bandages, disentangles the complexity of innovation processes in medicine and the incorporation of AI in healthcare as a tool to strengthen the professional-patient relationship as opposed to the existing telemedicine format of the primary contact. The intention, as well as the hope, is to reconnect the fragmented research context of smart bandage prototyping for the treatment and control of chronic wounds. As one of the major medical issues that involve constant monitorisation, DCWs are the centre of interest to several stakeholders. From patients, caregivers, and medical services providers involved in their monitoring and treatment to engineers, material scientists, and designers involved in the products used to treat them. Smart Bandages are the starting point of reflection on the nature of prototyping and the place of care in the current design processes for medical products. Moreover, by assessing the logic of AI care in the development of these devices, the present article can offer insights into how user-centred design practices are run nowadays, as well as how they could be done in the near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".