Scan and replicate human body parts for more accurate implants. This technology has potential to work with teeth, bones and other implants that need precision production to minimise any negative impact on the host body. There may be production benefits achievable in the future.

Automated equipment to assist patients and healthcare practitioners with physical mobility, supplemented by artificial intelligence. Often implemented in therapy and rehabilitation or assisted living facilities. Includes aritificial limbs, walkers, wheelchairs etc.

Automated equipment to assist patients and healthcare practitioners with physical mobility, supplemented by artificial intelligence. Often implemented in therapy and rehabilitation or assisted living facilities. Includes artificial limbs, walkers, wheelchairs etc.

People experience side effects on drugs or procedures across the globe and it can be hard to gather the information needed to improve and refine both products and services. AI can be used to collate patient data from departments such as A&E and ultimately use this to predict the impact and help set procedures to minimise these side effects. For example, AI can be used to enhance procedures so as to minimise exposure to potentially dangerous substances during MRI scans.

Image analysis or GCMS analysis in a high throughput manner. Applications of GCMS include drug detection, fire investigation, environmental analysis, explosives investigation, and identification of unknown samples.

Measuring key physical indicators - for example motion and gate - to diagnose potential medical complications

Monitoring of body (eg iris photos or skin conditions) to predict potential for issues of medical concern. May be time relapse dependent (eg size of mole over several days) or point-in-time diagnosis (e..g iris photos). Can be deployed as an app in some circumstances.

Provide support for medical teams to triage incoming patients relative to their medical symptoms and history and personal situation. Data used may include audio, video, comparable, historic and real-time data.

Optimise labour staffing and resource allocation to reduce bottlenecks in medical institutions - in many hospital systems significant time for example is spent researching into, and managing, bed availability.

Predict personalised health outcomes to optimise an individual's recommended treatment package. The data required to support this is often still being understood and developed.

In a world of growing data availability and machine learning medical practitioner performance indicators can be better measured. These can include overall outcomes as well as individual actions (e.g. prescription practices) and the data can be more easily standardised.

Predicting prescription adherence with different approaches to reminding patients

Analyse body or breath odour to diagnose potential disease. Sensitive tools can be used to capture and then analyse odours which are beyond the range of the human olefactory system.

Whether babies in cots, horses in stables or indviduals in hospital beds CCTV can be used to monitor behaviour, stance and audio to predict potential issues or healthcare related abnormalities. Predictive messages can then be sent to trigger human intervention. CCTV reduces some of the issues associated with wearables although clearly only works in defined spaces.

Keeping track of various treatment options - prescriptions, plans etc using sensors to automate billing and administration

Medication (dosage) effectiveness will potentially vary by individual and circumstance. Machine learning can help build individualised healthcare pathways.

Predict likely outcome risk for patient to support healthcare providers and affected individuals (and family) with planning, correct treatment pathways and recommended responses, including palliative care support.

Identify fraud, waste, and abuse patterns from a variety of clinical operations data. This would typically occur at a cross-functional level, but could be at an individual, operating unit or institutional level.

Predicting changing health patterns in a population based on data including which health services are accessed and how often, patient treatment outcomes, demographics etc. Allows for healthcare system resource planning.

Predict prescription addiction and abuse using data from both individuals and broader historic pattern data. This is especially impactful where there are clusters of cases in similar locations.

Automated medical response to individuals with health questions available through media including apps, online and chat functionality. Potentially used as part of a triage functionality in a healthcare system.

Ensure high quality record keeping by transcribing key medical conversations - lowering risk of potential errors due to (famously) poor medic handwriting. Provides detailed, comprehensive information capture.

Behavioural nudges (eg reminders) to encourage prescription intake and positive steps towards healthy habits specifically for managing chronic health conditions. Personalisation of messaging pathways will help those in what may be a painful and lonely situation (although not as much as sympathetic personal contact....).

Assist physicians by providing the latest medical information and research to inform proper patient care - this may be from specialist providers and cover the sorts of rare ailments that medical staff may not encounter on a regular basis

Detect major trauma events from wearable sensor data and signal emergency response. Real time sensors are used to monitor individual symptoms to alert emergency staff to urgent health issues. This can be extended to analysing incoming phone calls for audible clues.

Claims review prioritisation to ensure that cases are handled effectively and efficiently.

Diagnose injury (eg brain trauma) from visual scans

Personalise messaging and approach to improve wellness and adherence to prescriptions and medical programmes - this can be delivered as a 'nudge' programme

Monitor and react to health symptoms in post-hospitalisation care programmes, using wearable sensors and data captured on smart devices. Machine analysis of the data will potentially trigger alerts to medical personnel for active intervention.

Leveraging molecule database with metabolic stability data to elucidate new stable structures