All healthcare providers share the goal of treating more patients, cutting the cost of healthcare, and achieving better patient and business outcomes. With these goals in mind, many providers are now embracing solutions for artificial intelligence. AI promises to help healthcare providers deliver better outcomes by improving preventive medicine, enhancing diagnostics and enabling clinicians to treat more patients. With AI applications and systems, healthcare providers can easily sift through large amounts of data to identify infections sooner, predict which patients are likely to have certain problems and identify needs in large groups of people. At the same time, AI can help providers optimize the use of existing resources to improve productivity and contain costs.

In today's hospitals and healthcare clinics, a new doctor's new assistant is now often on the job -- in the form of artificial intelligence. Whether it's analyzing medical images or guiding robots that assist with surgeries, AI is making steady inroads into our hospitals and clinics. Need an online nursing assistant or a watchdog that helps detect dosage errors? The advent of AI in healthcare is a promising trend in terms of both patient care and economic efficiency. AI can help us address a forecasted shortage of physicians, particularly in specialty-care fields, while containing the costs of caring for an aging and growing population.[1]

A study published in The Lancet Oncology establishes for the first time that artificial intelligence can process medical images to extract biological and clinical information. By designing an algorithm and developing it to analyse CT scan images, medical researchers at Gustave Roussy, CentraleSupélec, Inserm, Paris-Sud University and TheraPanacea have created a so-called radiomic signature. This signature defines the level of lymphocyte infiltration of a tumour and provides a predictive score for the efficacy of immunotherapy in the patient. In the future, physicians might thus be able to use imaging to identify biological phenomena in a tumour located in any part of the body without having to perform a biopsy. Until now, no marker could accurately identify those patients likely to respond to anti-PD-1/PD-L1 immunotherapy in a situation in which only 15 to 30 percent of patients do respond to such treatment.

"Immunotherapy has profoundly changed the management of multiple cancers," said Roger Sun, MD, PhD candidate under Eric Deutsch, MD, PhD, and Charles Ferté, MD, PhD, at the laboratory INSERM U1030 at Gustave Roussy in Villejuif, France. "However, most patients do not respond to this type of treatment. That is why we need to identify biomarkers that allow identification of patients who are most likely to respond to immunotherapy." Studies utilizing biopsy samples of tumor tissues have confirmed the link between immune-cell infiltration into tumors and patients' treatment responses; however, Sun noted, because cancers are heterogeneous, biopsies only reflect the local aspect of the tumor. "Medical computational imaging, also known as radiomics, is a new field of research that aims to translate standard imaging like CT, MRI, or PET into objective data and use them as biomarkers," he explained.