Artificial intelligence and digital transformation in health
Introduction
Artificial intelligence (AI) has become widely accepted as a transformative innovation with proven capabilities in performing diagnosis of medical conditions beyond input from clinicians. The capacity of AI systems includes the enhanced abilities to learn from patient records and genomic information. It is progressively developing as an area of interest for regulators in the healthcare industry. It is described as an emerging enabling technology with a potential to address deficiencies in healthcare systems and improve patient outcomes (Arora, 2020). Several companies across different industries have embraced digital transformation as the role of technology shifts to enabling business drivers. A large global pharmaceutical company described it as an opportunity for medical affairs centred on patient centricity to enhance patient care (Smith, 2019). Digital transformation broadly offers changes in the future for the pharmaceutical and healthcare industries. This paper aims to outline current uses of AI and digital transformation in healthcare, as well as the trends and insights shaping the future landscape.
Overview of uses of AI and digital transformation in healthcare
The global pharmaceutical industry has relied on conventional tools to determine potential drug candidates that will successfully pass through drug development stages. This can become a costly and time-consuming process due to increasing challenges in identifying suitable drug compounds. As a result, the industry is keen to explore AI tools for drug development. Majority of AI based drug discovery tools utilise machine learning which can work with organised and labelled small data sets that can recognise features of a cell for example. Scientists believed it has the potential to improve drug development by tackling different aspects of the drug discovery process for identification of promising candidates, gaining regulatory approval and speeding up the process (Freedman, 2019). A growing number of big pharmaceutical companies have announced partnerships with AI – based start-ups to develop drug discovery collaborations.
Below is an outline of the emerging uses and applications in the sector documented in a recent publication (Bohr and Memarzadeh, 2020).
- Precision medicine tailoring interventions to specific patients based on profile including disease area, diagnostic information or treatment using advanced models. There are different types including complex algorithms using large data sets like genetic information, demographic data or electronic health records to predict prognosis and optimal treatment strategy. Also, digital health applications that record and process data added by patients to find trends and give personalised advice.
- Genome sequencing offered to a large part of the population as a tool interfacing genomic and phenotype information. The aims are to link genetic data sets relating to disease markers. Therapeutic targets are identified with the purpose of developing individualised genetic medicines.
- Drug discovery and development to improve the productivity and efficiency of the drug innovation process. Increasing new models and machine learning techniques have shown great potential to aid drug discovery by streamlining tasks. These techniques have been used in assessing biological activity, pharmacokinetics characteristics, toxicity, chemical properties of drug molecules and drug target interactions.
- Medical visualisation and imaging for interpreting images and videos for diagnosis and guided surgery.
- Augmented Reality (AR) and Virtual Reality (VR) which can be implemented in different areas. This is important for education and exploration through interaction with the surroundings for further understanding. The benefits enhance learning experiences for medical and health related disciplines particularly in training. The other perspective is the patient experience for example the use of immersive VR for patients with chronic conditions to allow engagement and help in coping with pain or discomfort.
- Personal health records to enable patient lead functionality switch promotes self-management to improve outcomes hand implementation based on patient centricity.
- Wearables and health monitoring which includes health devices that allow measurement of vital signs on under different conditions who stop the advantages are the flexibility they offer users to track activities with a sense of control over their helpful stop the devices can bridge a gap between multiple healthcare professionals and scientific disciplines.
- Robotics and AI powered devices used to replace workforce to augment human abilities and assist healthcare professionals. There are also robots used for minimally invasive surgical procedures, rehabilitation, implants, prosthetics and