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The integration of Artificial Intelligence (AI) in healthcare signifies a substantial shift, offering benefits to patients and healthcare systems while also introducing new risks. The emphasis on patient safety and performance standards is pivotal, especially with the European Union's strides towards regulating AI through the AI Act. This act focuses on classifying AI systems based on risk levels, mandating stringent requirements for high-risk AI, enhancing transparency, and ensuring ethics in AI applications. The concept of an "AI passport" is introduced as a living document detailing the AI system's purpose, ethical declarations, training, evaluation, and potential biases. This passport aims to enhance transparency and safety in medical AI applications, serving as a comprehensive record for patients, clinicians, and stakeholders. The AI passport, structured in JSON format, encapsulates key information about the AI system as a mechanism for continuous performance evaluation and transparency. This initiative may represent a significant step towards mitigating the risks associated with AI in healthcare, emphasizing the importance of accountability, transparency, and patient safety in the development and application of AI technologies. [ABSTRACT FROM AUTHOR]

BackgroundObesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps. ObjectiveThis study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations. MethodsWe first studied the users’ needs and gathered users’ graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility. ResultsWe collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m2 (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities. ConclusionsThe Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives.

As there is now a growing interest in mHealth apps for cancer patients, we here present and test the Lalaby App to monitor lung cancer patients' Quality of life (QoL) through mobile sensors and integrated questionnaires. The app was used in a 2-week study to register two lung cancer patients' activity without problems or interruptions. The patients frequently reported activities, symptoms, and questionnaires, indicating their engagement with the app. They registered their experience through the UEQ-S integrated into the app. Patient 1 mainly reported a neutral experience, while Patient 2 found it highly positive. They considered the app leading-edge and helpful and would recommend it to others, while both patients valued it positively (3.72 and 4.64 on a scale of 1–5). The app's aesthetics and its notifications helped their engagement. We found correlations between sensors' data and patients' QoL. We also detected QoL and functional status variations after treatment for both patients. After a "Tasks Test," two oncologists assessed the app's dashboard usability as excellent (SUS scores 85 and 87.5 on a 0–100 scale), easy-to-use and helpful. Their experience was positive (UEQ-S overall scale 2.81 (mean), −3 to +3 scale). The app allows monitoring the QoL of lung cancer patients remotely and in real-time while controlling patients' experience to stop the use if necessary, avoiding overwhelm. [ABSTRACT FROM AUTHOR]

Background High-grade glioma (HGG) patients present with variable impairment in neurocognitive function (NCF). Based on that, isocitrate dehydrogenase 1 (IDH1) wild-type HGGs are more aggressive than IDH1 mutant-type ones, we hypothesized that patients with IDH1 wild-type HGG would exhibit more severe NCF deficits than their IDH1 mutant counterparts. Methods NCF was assessed by Mini Mental Status Exam (MMSE), Trail Making Test (TMT), Digit Span (DS), and Controlled Word Association Test (COWAT) tests in 147 HGG patients preoperatively. Results Analyses between IDH1 groups revealed a significant difference on MMSE concentration component (p ≤.01), DS (p ≤.01), TMTB (p ≤.01), and COWAT (p ≤.01) scores, with the IDH1 wild group performing worse than the IDH1 mutant one. Age and tumor volume were inversely correlated with MMSE concentration component (r = −4.78, p <.01), and with MMSE concentration (r = −.401, p <.01), TMTB (r = −.328, p <.01), and COWAT phonemic scores (r = −.599, p <.01), respectively, but only for the IDH1 wild-type group. Analyses between age-matched subsamples of IDH1 groups revealed no age effect on NCF. Tumor grade showed nonsignificance on NCF (p >.05) between the 2 IDH1 mutation subgroups of grade IV tumor patients. On the contrary, grade III group showed a significant difference in TMTB (p <.01) and DS backwards (p <.01) between IDH1 subgroups, with the mutant one outperforming the IDH1 wild one. Conclusions Our findings indicate that IDH1 wild-type HGG patients present greater NCF impairment, in executive functions particularly, compared to IDH1 mutant ones, suggesting that tumor growth kinetics may play a more profound role than other tumor and demographic parameters in clinical NCF of HGG patients. [ABSTRACT FROM AUTHOR]