The UK's National Health Service has announced that it will test delivering vital chemotherapy drugs via drone to the Isle of Wight. The body has partnered with Apian, a drone technology startup founded by former NHS doctors and former Google employees. Test flights are due to begin shortly, and it's hoped that the system will reduce journey times for the drugs, cut costs and enable cancer patients to receive treatment far more locally. The Isle of Wight is an island two miles off the south coast of England with a population just under 150,000. Due to the short shelf-life of most chemotherapy drugs, medicines are either rushed onto the island or patients take the ferry to the mainland.

Earlier this year, a 71-year-old man in the Swedish city of Trollhattan was shovelling snow outside his house when he suffered a cardiac arrest -- his heart suddenly stopped beating. A passing doctor rushed to help and began performing cardiopulmonary resuscitation (CPR) to keep blood and oxygen flowing to his brain and other vital organs, while a bystander called for an ambulance. It was a race against time. With no pulse, the man's heart needed to be shocked back into life using a defibrillator. The drone took just three minutes to arrive, reaching the patient well ahead of paramedics.

The role of AI has modified considerably – from its preliminary creation on the threshold of an enterprise of their innovation labs to the modern-day while human beings are starting to recognize that it has the ability to convert businesses from the center out. According to the reports, AI will be better than human beings in translating languages by 2024, promoting items by 2031, and conducting surgical procedures by 2053. Meanwhile, let's see the changes that AI brings in 2022: Machine Learning (ML) is an application of artificial intelligence that gives systems the capacity to automatically analyze and enhance from experience without being explicitly programmed. ML specializes in the development of computer applications that may access information and use them to analyze for themselves. ML is the idea that computer software can learn and adapt to new information without human intervention.

Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of life. This study focuses on predicting the development of heart failure in cancer patients after cancer diagnoses using historical electronic health record (EHR) data. We examined four machine learning algorithms using 143,199 cancer patients from the University of Florida Health (UF Health) Integrated Data Repository (IDR). We identified a total number of 1,958 qualified cases and matched them to 15,488 controls by gender, age, race, and major cancer type. Two feature encoding strategies were compared to encode variables as machine learning features. The gradient boosting (GB) based model achieved the best AUC score of 0.9077 (with a sensitivity of 0.8520 and a specificity of 0.8138), outperforming other machine learning methods. We also looked into the subgroup of cancer patients with exposure to chemotherapy drugs and observed a lower specificity score (0.7089). The experimental results show that machine learning methods are able to capture clinical factors that are known to be associated with heart failure and that it is feasible to use machine learning methods to identify cancer patients at risk for cancer therapy-related heart failure.

A new portable device that utilizes artificial intelligence (AI) and biosensors can rapidly detect if cancer cells thrive after chemotherapy treatment. A team of researchers at Rutgers University has designed a new portable device that is up to 95.9 percent effective and accurate in counting living cancer cells when they pass through certain electrodes. This way, doctors can see if the targeted chemotherapy treatment was effective. Published in the journal Microsystems & Nanoengineering, the study aims to devise a new and simple method to rapidly assess drug efficacy in targeted chemotherapy cancer therapy, where anticancer drugs are conjugated to antibodies that target surface markers on cancer cells. Cancer continues to be one of the leading causes of mortality and death across the globe.

The selection of a first-line chemotherapy drug to treat many types of cancer is often a clear-cut decision governed by standard-of-care protocols, but what drug should be used next if the first one fails? That's where Georgia Institute of Technology researchers believe their new open source decision support tool could come in. Using machine learning to analyze RNA expression tied to information about patient outcomes with specific drugs, the open source tool could help clinicians chose the chemotherapy drug most likely to attack the disease in individual patients. In a study using RNA analysis data from 152 patient records, the system predicted the chemotherapy drug that had provided the best outcome 80 percent of the time. The researchers believe the system's accuracy could further improve with inclusion of additional patient records along with information such as family history and demographics.