At the heart of this congress lies the ambition to cultivate a dynamic community of practice dedicated to harnessing artificial intelligence (AI) for enhancing clinical decision-making in cancer treatment. This objective goes hand in hand with ESMO’s commitment to establishing and disseminating rigorous standards, alongside practicable guidelines, to ensure the safe and ethical application of AI technologies. This strategic vision is embodied by the ESMO AI and Digital Oncology Hub, a novel platform designed to foster interdisciplinary dialogue, facilitate the sharing of critical resources, and guide oncological professionals through the multifaceted opportunities and risks associated with AI’s integration into their work.

The program curated for the AI&DO Congress reflects a sophisticated blend of innovative research and clinical relevance, structured to maximize participant engagement without the distractions of overlapping sessions. Attendees will delve into the frontier of multimodal AI applications for clinical decision support, with particular focus on molecular tumour boards. The capacity of AI systems to integrate and analyze real-world data to augment personalized treatment strategies promises to revolutionize patient outcomes and healthcare efficiencies.

A focal point of the congress will be the exploration of AI-driven biomarkers in precision oncology. This segment emphasizes the development of validation frameworks and predictive modeling techniques that enable accurate and timely identification of actionable biomarkers. A highlight of this discourse will be the imminent launch of the ESMO Basic Requirements for AI-based Biomarkers in Oncology (EBAI) framework, signifying the first comprehensive guidance specifically tailored for the clinical validation and application of AI-assisted biomarker analyses.

Moreover, generative AI technologies and large language models—exemplars of cutting-edge advancements in machine learning—will take center stage. Esteemed keynote speakers from preeminent institutions such as Google DeepMind and Harvard Medical School will provide deep insights into how these tools are reshaping research methodologies, clinical workflows, and patient communication within oncology. Their expertise will shed light on both the transformative potential and prudent considerations surrounding responsible AI deployment.

The congress further incorporates practical sessions on digital health innovations, spotlighting wearable technologies, telemonitoring, and decentralized clinical trials. These discussions will unpack how continuous patient data acquisition and remote health assessments translate to more adaptive and patient-centered cancer care models. By leveraging AI to interpret these rich data streams, clinicians can more accurately monitor disease progression and treatment responses.

Ethical concerns, regulatory frameworks, and patient perspectives form another pivotal theme of the event. As AI becomes increasingly embedded in oncology, establishing trust, ensuring explainability, and understanding societal implications are paramount. The congress will critically evaluate current policies and propose pathways to reconcile technological innovation with patient rights, data privacy, and equitable healthcare access.

The industry dimension of AI and digital oncology will also feature prominently, with leading technological innovators unveiling their latest advancements. This synergy between academic research and commercial development is poised to accelerate the translation of AI breakthroughs into everyday clinical tools, transforming cancer diagnosis, prognosis, and therapy management.

One of the congress’s most anticipated milestones is the forthcoming publication of the ESMO Basic Requirements for AI-Based Biomarkers in Oncology (EBAI), destined to appear in the Annals of Oncology. This document represents the first comprehensive framework guiding the rigorous validation, clinical safety, and efficacy standards for AI biomarkers, bridging the gap between computational innovation and clinical utility.

The EBAI framework builds substantially on ESMO’s earlier efforts, including their pioneering ELCAP guidance, which set foundational standards for the safe use of large language models within oncology. These contributions reflect ESMO’s proactive and thought-leadership role in navigating the complexities of cancer care’s digital transformation, ensuring that AI adoption aligns with the highest standards of patient safety and clinical effectiveness.

Throughout the congress, attendees will have diverse opportunities to engage in high-level discussions, collaborate on new research agendas, and network with global experts spanning oncology, artificial intelligence, regulatory sciences, and bioethics. This gathering envisions a future where AI not only augments clinical intelligence but also democratizes access to cutting-edge cancer treatment worldwide.

ESMO’s commitment to seamless information dissemination is underscored by their provision of media engagement opportunities, allowing for broad public and academic outreach. This inclusive approach aims to spark further interest and investment in AI-driven oncology research, ultimately fostering innovations that can translate into improved patient survival and quality of life.

In essence, the ESMO Congress on Artificial Intelligence and Digital Oncology marks a pivotal chapter in the ongoing digital evolution of cancer care. By weaving together multidisciplinary expertise, rigorous scientific inquiry, and thoughtful ethical considerations, this event is positioned to usher in a new era where AI-powered precision medicine becomes an integral part of the oncology landscape—saving lives and redefining the possibilities of modern health care.

Subject of Research: Artificial Intelligence applications and digital innovations in clinical oncology.

Article Title: ESMO Launches Groundbreaking Congress on Artificial Intelligence and Digital Oncology, Setting New Standards for Cancer Care.

News Publication Date: 12 November 2025.

Web References:
– ESMO AI & Digital Oncology Congress 2025: https://www.esmo.org/meeting-calendar/esmo-ai-and-digital-oncology-congress-2025
– ESMO AI and Digital Oncology Hub: https://www.esmo.org/newsroom/esmo-ai-digital-oncology-hub
– ESMO Basic Requirements for AI-based Biomarkers in Oncology (EBAI) [forthcoming in Annals of Oncology]
– ELCAP guidance on large language models in oncology: https://www.annalsofoncology.org/article/%20S0923-7534(25)04698-8/fulltext

Keywords: Artificial intelligence, Cancer, Precision oncology, Biomarkers, Digital health, Generative AI, Large language models, Ethics in AI, Clinical decision support, Molecular tumour boards, Remote monitoring, Decentralized clinical trials.

As AI continues to permeate various sectors, its role in healthcare is particularly significant. From diagnostic processes to patient management, AI technologies promise enhancements in efficiency and accuracy. However, the findings of this study emphasize a crucial hurdle: trust. Many adults appear reluctant to embrace AI tools, potentially limiting the benefits these technologies could bring. The survey was conducted over a period from June to July 2023 and comprised a nationally representative sample, lending credence to the results and underscoring the widespread nature of this concern.

An interesting demographic insight revealed by the study is that female respondents were notably less likely than their male counterparts to place trust in their healthcare systems’ ability to employ AI responsibly. This variation suggests that gender may play a significant role in the perception of and trust in technology, raising questions about the potential implications for healthcare policy and patient engagement strategies. It indicates that healthcare providers must consider these demographic factors when communicating about AI technologies.

In exploring the reasons behind the substantial distrust in AI, the researchers found that overall health literacy or knowledge about AI did not correlate with trust level. This disconnect highlights a significant gap in communication and engagement strategies employed by health systems. It suggests a need for healthcare providers to actively engage with patients, providing them with comprehensive information and fostering dialogue about the use of AI in patient care. This raises an important question: how can healthcare systems build trust in the face of rapidly advancing technologies?

Building trust may require more than mere information dissemination; it necessitates meaningful engagement and transparency. Health systems might need to rethink their strategies, focusing on patient education efforts that illuminate not just how AI works, but also how it is safeguarded against potential harms. Establishing a transparent dialogue can empower patients to feel more confident in the healthcare technologies they are being offered, thus improving their overall satisfaction and engagement with care processes.

The authors of the study advocate for future research to examine the trajectory of trust over time, especially as patients become more familiar with AI technologies. They emphasize the importance of longitudinal studies that track shifts in public perception, as familiarity with AI tools could lead to increased trust levels. This points to an important opportunity for healthcare systems to evolve their communication strategies in response to changing patient perspectives as they navigate the complexities of AI integration.

Another vital recommendation from the research is the imperative for healthcare systems that adopt AI to enhance their communication regarding the tools they use in patient care. This enhancement could involve systematically addressing concerns, sharing success stories, and highlighting safeguards in place to protect patient welfare. Such communication could alleviate some of the fears associated with AI, ultimately fostering a more trusting relationship between patients and their healthcare providers.

The impact of AI in healthcare is set to grow. As technological innovations continue to be integrated into clinical practices, addressing trust issues will become increasingly critical. Healthcare systems that can successfully navigate these concerns will not only provide better care but also position themselves as leaders in the responsible use of AI. By prioritizing patient trust and fostering open communication, they may ultimately pave the way for more widespread acceptance and utilization of AI technologies.

An essential aspect of the discourse on AI and healthcare is the ethical considerations that accompany its use. This involves not only the technical capabilities of AI but also the moral obligations of healthcare entities to prioritize patient safety and autonomy. The integration of AI should not come at the cost of the trust relationship between patients and healthcare providers; thus, ethical considerations must remain at the forefront as these technologies evolve.

As AI technology continues to advance rapidly, it is essential for researchers, healthcare providers, and policymakers to collaboratively address the emerging concerns surrounding trust and safety. This multidisciplinary approach will ensure that AI serves as a tool for improvement rather than a source of apprehension. In the arena of healthcare, where trust serves as the bedrock of effective patient-provider relationships, fostering a culture of transparency, ethical responsibility, and continuous dialogue will be paramount.

In conclusion, the study published in JAMA Network Open serves as a clarion call for healthcare systems to reinforce their commitment to ethical practices and patient engagement in the age of AI. It implores these systems to recognize the profound implications their integration of technology has on public trust, highlighting the need for a proactive approach to communication and education. As AI continues to shape the future of healthcare, the onus lies on healthcare providers to earn and sustain the trust of their patients.

By committing to these principles, healthcare systems can navigate the complexities of AI integration while ensuring that patients feel secure and valued in the care they receive. The insights from this research not only reveal gaps in public perception but also offer a roadmap for improving the relationship between patients and healthcare providers in this new technological landscape.

Subject of Research: Trust in healthcare systems using artificial intelligence
Article Title: Patients’ Trust in Health Systems to Use Artificial Intelligence
News Publication Date: [Date Not Provided]
Web References: [Web References Not Provided]
References: [References Not Provided]
Image Credits: [Image Credits Not Provided]

Keywords: Artificial Intelligence, Healthcare, Trust, Patient Safety, Communication, Ethical Considerations, Health Literacy, Gender Differences, Public Perception, Technological Integration, Patient Engagement, Research Study.