Corticosteroids and macrolide immunomodulators such as tacrolimus are effective drugs for the topical treatment of inflammatory eye diseases like allergic conjunctivitis or dry eye. However, tacrolimus is practically insoluble in aqueous solutions and is therefore currently formulated as dispersion. This leads to low bioavailability. Here, we present a novel pharmacologically acceptable, aqueous formulation of tacrolimus based on the "Marinosolv formulation platform". Marinosolv allows the solubilization and thereby improvement of the bioavailability of many otherwise practically insoluble drugs, since dissolved drugs permeate faster into tissues, including ocular tissues. To visualize the benefits of Marinosolv in ophthalmic formulations, we investigated the permeation of a fluorescently labeled estradiol dissolved in Marinosolv compared to a formulation containing the compound as dispersion. Permeation was studied ex-vivo and in-vivo in porcine eyes. Further, we evaluated the improved permeation of topically applied tacrolimus dissolved in Marinosolv compared to a commercially available topically applied tacrolimus dispersion. The Marinosolv formulation was also compared to oral tacrolimus treatment, the standard application route for this drug in case of severe posterior uveitis. Finally, the ocular tissue levels of tacrolimus in all groups were determined using HPLC/MS. We demonstrated that tacrolimus dissolved in Marinosolv reached significantly higher levels in ocular tissues compared to the marketed topical product or after oral application and thus may be a suitable novel option for the treatment of several eye diseases, such as allergic conjunctivitis or uveitis. Thus, Marinosolv may be considered as a new vehicle for tacrolimus eye drops.

A central requirement for the use of artificial intelligence (AI) in medicine is its explainability, i. e., the provision of addressee-oriented information about its functioning. This leads to the question of how socially adequate explainability can be designed. To identify evaluation factors, we interviewed healthcare stakeholders about two scenarios: diagnostics and documentation. The scenarios vary the influence that an AI system has on decision-making through the interaction design and the amount of data processed. We present key evaluation factors for explainability at the interactional and procedural levels. Explainability must not interfere situationally in the doctor-patient conversation and question the professional role. At the same time, explainability functionally legitimizes an AI system as a second opinion and is central to building trust. A virtual embodiment of the AI system is advantageous for language-based explanations

The current research evaluates user experience and preference when interacting with a patient-reported outcome measure (PROM) healthcare application displayed on a single tablet in comparison to interaction with the same application distributed across two tablets. We conducted a within-subject user study with 43 participants who engaged with and rated the usability of our system and participated in a post-experiment interview to collect subjective data. Our findings showed significantly higher usability and higher pragmatic quality ratings for the single tablet condition. However, some users attribute a higher level of presence to the avatar and prefer it to be placed on a second tablet.

The collection of patient-reported outcome measures (PROMs) is a key measurement tool for patient-centred care. At the same time, collecting these measures poses obstacles for many patients, leading to these groups being underrepresented in the data. We have therefore developed a multimodal, AI-driven assistance system to support patients in collecting these data. The interface of the system comprised a digital tablet containing the PROM questionnaire items and the assistant in three forms of embodiment: A virtual avatar, a physical avatar, and a voice-only agent. To evaluate the users’ experience and ratings of the system, two separate studies were implemented in two rehabilitation centers with 195 patients. A mixed within–between RCT was conducted at an outpatient clinic, where patients completed PROMs both with and without an assistant, and a between-subject design at an inpatient clinic comparing routine PC-based care with avatar- and robot-assisted PROM administration. Our results suggest a preference for the non-assisted tablet-only condition in Clinic A, whereas, in Clinic B, both agent conditions were preferred over routine care. We have further analyzed aspects such as trust and social presence in this study to gain a more thorough understanding of the users’ experience. Our analysis shows a higher trust rating for the voice-only assistant, whereas the robot, virtual avatar, and the voice-only conditions were perceived as more socially present. The impact of demographic factors and affinity for technology on the user ratings was also thoroughly studied. Our findings shed light on the role of agent embodiment in PROM assistance and contribute to the future design and evaluation of effective, engaging, and trustworthy systems for data collection in healthcare settings.