Building Trustworthy AI for Clinical Use: A Start-Up Success Story

Aisthesis Medical – AIMED, a pioneering MedTech startup, is at a reasonable level of digital maturity compared to other European SMEs, especially if you take into account, they are relatively new. However, during the assessment of its digital maturity we identified several areas of improvement. The following needs were identified: 

• Need to receive funding

• Need to increase the level of technological readiness

• Harmonisation with current copyright regulations

• Lack of universal data collection protocol

• Lack of continuous training of staff

• Reduction of the company’s environmental impact

The results from the first DMA highlighted the importance of focusing on the following areas:

• Copyright protection: it would be important to safeguard the intellectual property of its software, as well as its financial and moral rights. In addition, the company will have to register, if it has not already done so, its trademark with the Trademark Directorate of the General Secretariat of Trade and Consumer Protection of the Ministry of Development & Investment.

• Information/Training of Staff: AIMED staff should keep abreast of recent relevant technological developments through regular training programs, collaborations with academic and research institutions, and through participation in scientific conferences.

• Universal Data Collection Protocol: it is recommended that the company establishes a Universal Data Collection Protocol to collect data from various institutions and open databases.

• Maturation of Services/Products: the company should do so through seamless monitoring of market trends in the sector in which the company operates, and continuous adjustment of the company's growth plan on an ongoing basis.

However, in addition to the assistance required to increase AIMENT’s digital maturity indicators, the company was in need of recommendations and suggestions regarding their technical necessities, in order words they required help improving the technologies they provided.

The aim of the collaboration between AIMED and the Technical University of Crete was to on one side, to assess the company’s digital maturity level, and on the other, to inform them about the latest market trends and make suggestions for their further development. Additionally, the TUC established particular guidelines to develop a methodology for prediction in clinical. 

Thus, an action plan was developed to help incorporate algorithmic innovations to achieve accurate diagnosis. The services provided during this collaboration are divided into two categories: a) Data Preprocessing and b) Innovative Techniques in Machine/Deep Learning. 

Data Processing – this service involved following current regulations to manage the data and pre-process in order to reduce or even eliminate the noise introduced by abnormal activities, all while delivering the useful information. Moreover, it dealt with issues of data anonymization and de-noising, missing values, and generation of synthetic data, and data fusion. It is important to notice that, the data preprocessing stage is a key step before further analyses, ensuring the production of reliable and interpretable results in the field of medicine. 

Innovative Techniques – this service provided an extensive analysis of recent widely used methodologies. These methodologies revolved around innovative machine learning and deep learning algorithms. In this context, the following results were expected: prediction over inputs of a multimodal nature, automated machine learning techniques, parameter and hyperparameter optimization, specific interpretable AI approaches, and data auto-labelling techniques. 

Additionally, the rationale behind the required investment to finance the proposed solution is based on  AIMED’s financial benefits and returns already achieved, as well as those expected from its implementation. The development and application of the proposed methodology further means a reduction in the use of hardware resources.

The main benefits that the company has already seen from this collaboration relate to the information provided by TUC about recent market trends and the completion of the questionnaire regarding their digital maturity level. Benefits that are to be expected soon include:

• VIOSync has demonstrated >90% overall AI system performance, and strong predictive accuracy in multi-centre retrospective validation studies

• It is currently being piloted in NHS and EU hospitals with positive clinician feedback regarding usability and clinical trust

• The platform has been refined through co-design with frontline clinicians and sepsis survivors (via partnerships with UK Sepsis Trust and Sepsis Research FEAT)

• VIOSync is now part of high-profile accelerator and funding programmes, including EIT Health and Innovate UK

• This journey has positioned Aisthesis as a leader in building safe, scalable, and responsible AI for acute care — while delivering real value for patients, clinicians, and health systems.

• Copyright protection

• The company’s personnel will be provided with up-to-date information and receive the essential trainings in order to keep them informed about new technological developments related to the services and products provided by the company. The staff will have the opportunity to expand their knowledge by attending scientific conferences, cooperating with academic and research institutions, and completing additional training programs. 

• The development of a universal data collection protocol will contribute to the secure and rapid collection of data from various institutions and open databases. The objective is to achieve the collection of a wealth of data for analysis in a shorter period of time, thus pushing the company’s momentum.

• AIMED will achieve market consolidation through the creation of competitive products and services, and by maintaining constant dedication to this goal and keeping abreast of current trends.

In addition, the Technical University of Crete created specific guidelines to achieve AIMED’s objectives and to ultimately give them the tools to effectively and accurately predict sepsis. In this context, the company received consulting services in the form of two deliverables, with detailed descriptions of modern data preprocessing techniques and methodologies and innovative machine and deep learning techniques. The overall funding provided was close to twenty thousand euros which allowed the company to train their personnel in very advanced AI topics, protect intellectual property and use advances for medical monitoring and diagnosis.

Initially, there was a lot of discussion about better understanding the company's needs and the specific problems that needed to be addressed by the TUC. This step allowed for better communication between the parties when the time came to describe the problem and provide a solution. To give you an example, during the process we came across a real challenge, a tool used in difficult clinical environment, such as the ICU. In this case, initially we encountered a problem as the directions provided were too general, however, by better communicating with the client, we were able to understand why the offered service was important and where. 

Moreover, through this process we were able to identify common research interests that allowed us to grow our network and to joint forces for the presentation of research proposals.

As intensive clinical environments get more digital it is important to deploy such solutions but for ethical reasons. Large variability in patient data remains a major challenge and keep these solutions in a partial use.

• Trust must be designed in, not added later. Early collaboration with clinicians and patients is critical to making AI solutions clinically usable and ethically sound.

• Interoperability is a must, not a nice-to-have. Designing with standards like FHIR ensures that digital tools can plug into existing NHS systems without disrupting workflows.

• Regulatory readiness starts early. Navigating CE MDR, DTAC, and NHS procurement frameworks requires proactive planning, strong partners, and expert input — especially for early-stage SMEs.

• Explainability is a competitive advantage. Clinicians are more likely to adopt AI that shows its reasoning. Black-box tools face major trust and accountability barriers.

• Europe’s digital innovation network is essential. Collaborations with hospitals, EDIHs, regulators, and public-private programmes helped us move faster, smarter, and with more confidence.