Consumer-ported real time sensor devices (phones, watches etc) provide data for remote analysis. Analysis provided can be personal or via medical practitioner.

Physician attrition is a consistent issue in most medical systems given the innate pressures of the role. Downsides for the individual (emotional, physical, mental), their patients and their employers all need to be avoided.

Monitor data from biopsys to forecast likely medical implications. Rate of turnaround and accuracy of predictions are key factors.

Medical treatment plan optimised for an individual based on an analysis of their genetic data. The science is still at an early stage but deployment of AI will help speed up development.

Using NLP to describe the surrounding data collected and compiled using smartphones and Deep learning to help people with varied degrees of blindness navigate

Optimising matches between transplant patients and donors benefits all patients across the transplant waiting list as it results in more saved lives. For example, through paired kidney donations, AI is able to identify potential donors and recipients who are biologically suited for one another and can take into account certain criteria to optimise the service such as prioritising the hardest to match patients.

Using robotic sensors to capture surgeons' performances during robot-assisted surgery and analysing resulting performance data. This has a knock-on impact of potentially reducing medical malpractice liability exposure.

Defining tumour contours versus the surrouding healthy tissue to enable accurate and limited radioactivity deplpoyment is currently a manual task. This is painstaking work that is time-consuming and (given the cost of highly skilled surgeons doing the work) expensive which means that is rarely gets done between individual treatments and doctors approaches can differ (raising treatment risks). AI can deliver this at speed and economcially - which speeds up the treatment process and can be repeated.

Initially screening health insurance claims to detect incorrect claims

Sorting insects by sex enables aggressive intervention (e.g. sterilisation or culling) to minimise their ability to procreate and therefore spread disease. Machine vision enables this to happen at greater scale and pace therefore enabling more significant interventions. This also has research benefits.

Alerting and diagnostics from real-time patient data - this might include, for example, warning of a life-threatening incident such as a heart attack.

SignAll's solution provides every day assistance to deaf people by translating spoken English into Automatic Sign Language or vice-versa

Pregnancy management is a complicated challenge - especially where traditional support networks may no longer exist in the same way. Capturing health data potentially enables an algorithm to provide first line advisory support.

Using machine learning to predict readmission risk associated with each patient and suggest preventive care methods

Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU).

Modelling risk of long term ailment (e..g diabetes) development from relevant data. Data might include medical notes, real time sensor information, dietary or exercise feedback.

Predict risk of long term diagnosis of disease (e.g. heart attack in the next 10 years).

Analysis of clinical outcomes to adapt clinical trial design

Monitoring and prompting prescription compliance. This will impact both on patient recovery rates but also potentially lowers the risk of folllow-on issues such as antibiotic drug resistance.

Using NLP automatically translate into text or sign language or describe surroundings

Predict individual hospital admission rates using historical and real-time data. The output will hemp both individuals and institions affected but also overall capacity planning across medical organisations.

Using AI-powered robotics to enhance surgical operations. This will assist greater precision, reduces risks created by manual fatigue and ensures tight oversight and recall of the task in hand.

Analysing health insurance claims data to detect unusual patterns which may indicate fraud

Reduce hospital-acquired infections (e.g. pneumonia, sepsis, urinary tract infection and surgical site infection) by identifying risk and suggesting appropriate responses.

Diagnose known diseases from scans, images, biopsies, predictive analytics audio, and other data

Early diagnosis can significantly increase chances of recovery and reduce harm, but over-hasty medication can be harmful for the individual and expensive for the supplier. Predicting likely outcomes from very early stage symptoms can potentially deliver better results for all.

Assisted or automated diagnosis and prescription helps patients in response to easily capturable symptoms, or existing conditions. Whilst useful in developed medical systems the potential in underserved areas of the world could be transformative.

Analyse candidate patient data to measure probability of individual resistance to deployed drugs, typically projected over time and across a broader population.

Optimise medical product (devices, robots, drugs etc) launch strategy based on past launches and relevant data

Continual research and observation of the drug's effects on patients after the drug becomes generally available