Six-month outcomes in a French cohort of patients receiving a safety planning type intervention after suicide attempt.

Integrating genomic sequencing data into clinical practices is often standard and proven as a transformative step towards personalized healthcare that offers promising precision in disease diagnosis, prognosis, and customized therapeutic strategies. The importance of personalized healthcare has always been relentless in accurately predicting drug responses, especially in precision oncology, which cannot be overstated, as it directly influences treatment effectiveness, patient survival, and overall quality of life. However, the global healthcare system faces a growing shortage of trained professionals, with projections indicating a deficit of more than 15 million healthcare workers by 2030. This critical workforce shortage makes personalized healthcare delivery increasingly complex, particularly in data-intensive fields such as personalized medicine and pharmacogenomics, which reveal many customized treatment therapies. Highlights the urgent need for scalable, automated decision-support systems and integrative intelligent solutions. In this context, the convergence of Artificial Intelligence (AI), Blockchain (BC), and digital Health Informatics offers a promising pathway to bridge clinical gaps by enabling automated, trustworthy, reproducible, and verifiable predictive healthcare decision support systems (HDSS). The amalgamation of advanced large language models (LLMs) and AI systems into digital health has heralded a transformative era, positioning AI as a core technology in medical diagnostics and genomic prediction. In parallel, blockchain technology has emerged as a paired backbone solution to address critical concerns about data integrity, auditability, and trust in AI-driven predictions and decision-making. Recent studies show that generative AI can approach clinical diagnostic performance with several emerging challenges, i.e. lack of transparency and traceability that limit them for clinical deployment, triggering blockchain-based verifiability frameworks essential for building reliable and ethically compliant digital healthcare systems. Furthermore, AI has demonstrated momentous proven potential in the healthcare domain, yet they are frequently prone to hallucinations, misdiagnoses, and biases, especially when applied to complex, individualized treatment scenarios. Personalized medicine requires precise, context-sensitive reasoning based on the unique physiological profile and longitudinal medical history of the patient. When AI generates plausible sounds but factually incorrect outputs, the risk of inappropriate treatment recommendations becomes severe and potentially irreversible. These limitations emphasize the critical need for verifiable AI systems that not only produce AI predictions accurately, but also embed mechanisms for provenance, accountability, and error traceability capabilities, which need to be addressed according to the demanding urge of stakeholders and on-going research trends.