This thematic area relates to the'medicine of the future', principally the customisation of healthcare, with medical decisions, treatments, practises, or products being tailored to the individual patients, instead of a one‐drug‐ fits‐all model. Preventive or therapeutic interventions can then be targeted at those who will benefit, sparing expense and side effects for those who will not. Data analytics, including data mining and machine learning, is an integral part of the precision medicine model, e.g., in the discovery of new predictive or prognostic biomarkers or subgroups of patients. The number of papers reporting advances in this field are on almost an exponential rise since 2010 with Aaron Ciechanover, a Nobel Prize winner in Chemistry 2004, branding personalised medicine the "third revolution" of drug research. Neurodegenerative diseases, including Alzheimer's dementia (AD) and Parkinson's disease (PD), are caused by the progressive loss of structure or function of neurons.

Artificial intelligence (AI) and big data are transforming healthcare with high-throughput analyses of complex diseases. Machine learning and sophisticated computational methods can be used to efficiently interpret human genomes and other biomarkers, providing insights for patient treatment and with major applications in diagnostics and preventive care. A personalised treatment plan may include preventive care for diseases that are at a higher risk of developing, for example increased screening for cancer if a patient possesses the BRCA 1 or BRCA 2 gene mutation. Additionally, AI can generate insights from genetic information, biomarkers, and other physiological data to predict how a patient will respond to different treatment options, which may help avoid adverse reactions, reduce the use of expensive or unnecessary treatments on patients that are unlikely to respond, and ultimately reduce hospitalisation and outpatient costs. For more information, GlobalData's latest report, Precision and Personalized Medicine – Thematic Research, provides insight into the most prevalent uses of personalised medicine, new applications, and the healthcare, macroeconomic, and technology themes driving growth.

Technological progress has enabled unprecedented developments in the field of research. This is how the world of biology and medicine benefit from technological innovation. The application of computer science to the world of biology and medicine has been absolutely revolutionary for both branches and has helped significantly in the improvement of treatments. As indicated by the Instituto de Salud Carlos III, bioinformatics has been fundamental in the analysis and interpretation of SARS-CoV-2 data. During 2020, the research carried out by the Bioinformatics Unit of the aforementioned centre was essential, since it shed light on such important issues as the sequencing of the genome of the new coronavirus and the automation of diagnostic tests.

A multi-material 3D printing offers nearly endless possibilities for the spatial arrangement of individual materials within the object being printed. In the case of pharmaceutical tablets, the spatial arrangement of individual material domains containing the active pharmaceutical ingredients (APIs) and excipients uniquely defines the release profiles of the APIs. However, the inverse is not necessarily true – identical or very similar dissolution profiles can potentially be obtained from different tablet internal structures, implemented as a combination of domains containing excipients with different individual dissolution rates and different local API concentration. This work presents a computational method based on an Evolutionary Algorithm for the solution of the inverse problem, i.e. finding such tablet internal structure that results in a prescribed dissolution profile of each API contained in the tablet. After testing the algorithm on cases with a known solution, the methodology is applied to a problem of finding tablet structures that result in delayed release and step-wise release profiles, respectively.

Researchers at EMBL's European Bioinformatics Institute (EMBL-EBI), the Wellcome Sanger Institute, Addenbrooke's Hospital in Cambridge, UK, and collaborators have developed an artificial intelligence (AI) algorithm that uses computer vision to analyse tissue samples from cancer patients. They have shown that the algorithm can distinguish between healthy and cancerous tissues, and can also identify patterns of more than 160 DNA and thousands of RNA changes in tumours. The study, published today in Nature Cancer, highlights the potential of AI for improving cancer diagnosis, prognosis, and treatment. Cancer diagnosis and prognosis are largely based on two main approaches. In one, histopathologists examine the appearance of cancer tissue under the microscope.

IMAGE: A mosaic of tumour microscopy images forming broken DNA molecules. Researchers at EMBL's European Bioinformatics Institute (EMBL-EBI), the Wellcome Sanger Institute, Addenbrooke's Hospital in Cambridge, UK, and collaborators have developed an artificial intelligence (AI) algorithm that uses computer vision to analyse tissue samples from cancer patients. They have shown that the algorithm can distinguish between healthy and cancerous tissues, and can also identify patterns of more than 160 DNA and thousands of RNA changes in tumours. The study, published today in Nature Cancer, highlights the potential of AI for improving cancer diagnosis, prognosis, and treatment. Cancer diagnosis and prognosis are largely based on two main approaches.

Our second meeting of 2020 will focus on how Artificial Intelligence (AI) can be used in the field of genomics to help to develop personalised medicines and treatments to improve patient outcomes. AI has come a long way in health care and it has many different uses, one of those applied to genomics, by identifying individuals' phenotypes and genotypes health care professionals can offer personalised medicine, tailoring the right therapeutic strategy for the right person at the right time. AI can also determine an individual's predisposition to a disease, offering him/her timely prevention. The concept behind personalised medicine is to customize therapies to ensure they are tailored for patients based on their own unique genomic profile. But the cost of processing and storing every citizen's fully sequenced genome might be significantly costly.

Over half of NHS Trusts (52%) are already deploying artificial intelligence (AI) technologies, with 20% of them using them for clinical care, and 16% using them for clinical diagnosis, a Freedom of Information request has revealed. The data was obtained by a Freedom of Information request from cloud data services provider NetApp, with 61 Trusts responding, after the UK Government announced its investment in AI in the National Health Service (NHS). The request asked Trusts around their current and future use of AI-related technologies to deliver health services, and how mature their data infrastructures are in delivering successful AI projects. George Kurian, NetApp CEO and president, said: "Artificial intelligence has limitless potential in healthcare services and it's encouraging to see the technology being used in half of NHS Trusts. As healthcare moves towards preventative treatment and personalised medicines, artificial intelligence leaders in the NHS have a complex challenge to break through cultural and organisational barriers when it comes to providing healthcare professionals the access to data they require. "Progress is being made and the further deployment of AI-powered technologies – such as speech recognition and machine learning – will alleviate pressure on staff, accelerate innovation and reduce costs.

From automated eye scans to analysing the cries of new-born babies, faster drug development to personalised medicine, artificial intelligence (AI) promises huge advances in the field of healthcare. At the recent AI for Good Summit in Geneva, Switzerland, we were told how AI could speed up the development of new drugs, lead to personalised medicine informed by our genomes, and help diagnose diseases in countries suffering from underdeveloped health services and a chronic shortage of doctors. But there are two main obstacles preventing access to this utopian destination. One is that the AI being applied to the world's health problems isn't quite good enough yet. The other related issue is the lack of good quality digital data - less than 20% of the world's medical data is available in a form that AI machine learning algorithms can ingest and learn from, the WHO estimates.

AI in healthcare is already making waves with reports suggesting that artificial intelligence systems will generate $6.7 billion in revenue from the global healthcare industry. One particular area in healthcare that has significantly evolved is genomics. Comprising of areas such as gene sequencing and gene editing, this space is largely being explored in areas such as agriculture, animal husbandry and personalised medicine, among others. While researchers have been sequencing the gene and analysing DNA for a long time now, they face challenges such as the huge size of the genome, identifying regulatory elements, predicting gene function, high costs or technology limitations. Apart from that, the amount of data around genes and genomes in itself is humongous which is further added upon by the vast amount of patient data. Researchers have now been using machine learning in gene synthesis, understanding the genetic makeup of an organism, building precision and personalised medicines, and more.