Precision medicine holds great promise to improve the landscape of IBD course of care for an individual patient, providing the most beneficial therapy while minimising the risk. The ultimate goals of precision medicine include stratifying patients based on disease subtypes and severity, disease progression and treatment response using personal and clinical data coupled with molecular profiling data of patients.1 2 IBD, with its two main subtypes, Crohn's disease (CD) and UC, is a complex inflammatory disease with a wide range of contributing factors including host genetics, immune system, environmental exposures and the gut microbiome.3–5 The inherent complexity of the disease introduces a large number of confounding factors, which stand in the way of accurate diagnosis and precision medicine.6 The term'big data' is generally referred to as large volume of rapidly produced data from variable sources, known as the three'V's (volume, velocity and variety).7 Over the past decades, the production and availability of data that could inform healthcare has increased remarkably mainly due to technological advancements and falling costs of data generation.

Dr Pallavi Tiwari and her team at the Brain Image Computing Laboratory are using AI models to help move away from a one-size-fits-all approach to treating brain tumours. As technology continues to advance, its use within the healthcare sector is becoming more and more prominent. From remote healthcare to 3D printing, there are so many applications of technology that can pave the way for personalised medicine and better healthcare. One such application is the use of AI and machine learning in the treatment of brain tumours, something I spoke to Dr Pallavi Tiwari about. Tiwari is an assistant professor of biomedical engineering and the director of the Brain Image Computing Laboratory at Case Western Reserve University in Ohio.

Research at the Leiden University Medical Center (LUMC) Department of Pathology shows the power of artificial intelligence (AI) applied to endometrial carcinoma microscopy images. The group of Dr Tjalling Bosse offers insights that could improve diagnosis and treatment of uterine cancer. Their findings have been published in The Lancet Digital Health. Endometrial carcinoma is the most common cancer of the gynaecologic tract. At the LUMC both clinical trials and translational research is conducted to improve the care for these patients.

From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of highquality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient and use these results to individualize therapies. Research opportunities include adaptively planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge and to continually update that knowledge to benefit subsequent patients.

Cancer kills millions of people each year. From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of high quality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records, presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a “rapid learning” community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient, and use these results to individualize therapies. Research opportunities include: adaptively-planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge, and to continually update that knowledge to benefit subsequent patients. Achieving this goal is a worthy grand challenge for AI.