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Image by National Cancer Institute
Image by National Cancer Institute


use machines

From medical records, hospital records and physical examinations, to the stream of data generated by devices: the healthcare sector collects a sizable quantity of data. 

It’s clear that pharmaceutical and medical industries are perfect territories for the automation, categorisation and predictive might of machine powered tools.

Our highly specialised partners have dedicated their businesses to solving these complicated challenges.

Bespoke tools

Custom software solutions work in with your existing technical environments, without requiring your team to change how they work.

Specialist challenges

Every lab, every research project, every problem is different. Generic, off-the-shelf solutions weren't made for your specialist requirements. Specialist consultancies, on the other hand, are built to solve your challenges specifically.

Human touch

Consider how much of your day to day workload involves menial tasks that could otherwise be automated. Machine automations mean you can do more of the human suff.  

Machine speed

Really smart researchers spend days and days doing very repetitive work. This grunt work is a necessary part of the process. Machine learning solutions perform this work almost instantly – something that used to take two weeks can take five minutes.

Why Machine Learning?

Healthcare Capabilities

Applications of Artificial Intelligence, Applied to Healthcare

Healthcare Capabilities
Vasculature of the Heart

Machine Assistance 

How AI and machine learning can help across the healthcare landscape


Optimise clinical trails, drug efficacy screening, drug development and sequencing tasks, with artificial intelligence.


Powerful tools are available to enhance the accuracy and speed of diagnostic, with detection through pattern recognition.

Patient Engagement

Under ever increasing patient numbers and care standards, healthcare systems must adapt to become more efficient.

Hospital Admin

Bring machine speed and precision into administrative tasks to improve the flow and function of healthcare providers.

What’s your challenge?

Exceptional challenges in machine learning enhanced medical research

Medical practitioners have been understandably slow to accept machine learning as a technology in a discipline with such high stakes. 

This is despite the clear speed and precision gains that have surfaced in machine learning research. Machine learning must take a unique approach to healthcare because the burden of accuracy is far greater. This is in comparison to, for example, marketing or business intelligence.

The past few years of adoption have therefore not been one of capability acceptance, but rather a demonstration of responsible caution.


How are decisions made?

What steps have there been to remove bias?

What points of safety checks are built into deployed machine learning processes? 

Get in touch to find out how state of the art approaches address these questions. 


Drug discovery and clinical trials

Machine learning can be applied at every stage of drug discovery, including target validation,  prognostic biomarker isolation and in the clinical trial analysis of digital pathology data.


Speed up Drug Discovery, Save Lives

Development speed matters. 

In the case of drug development, reducing manual workflows and speeding up analysis literally translates to saving lives.

Get to publication stages faster with our partners, who have direct experience building software and writing code documentation to FDA-approval standard. 

Research has shown machine learning to be particularly effective in the outlined areas across these four key steps in discovery.

1) Target identification and validation

Identify and prioritise targets based on gene/disease correlations; predict druggability; identify alternative targets. 

2) Compound screening and lead discovery

Synthesise, screen and design compounds for desirable properties based on gene expression, proteomic and transcriptomic data.

3) Preclinical development

Biomarker identification and prediction of biomarkers of clinical end points.

4) Clinical development

Cellular phenotyping in oncology to determine drug response; precise measurements of tumour microenvironment.

Image by Science in HD

Select Better Participants

Predict clinical trial outcomes to predict futile efforts before wasting time and investment

The expectation of success is the key variable in valuing drugs in a drug development pipeline. 

Machine learning algorithms - often a combination of multiple algorithms - learn from past cases and ingest all data related to the trial and participants in order to forecast the success rate of a given trial. 

These predictions have been shown to improve on the more heuristic methods currently employed. 


Better detection, better decision

Successful treatment is only possible if it's targeting the right condition and it's caught early. Machine learning approaches recognise patterns faster and more accurately than humans can, so you can better decide how to treat.  


Data Analysis

Cut through noise and reveal underlying patterns across multiple data sources

Proteomics, biomarkers, genomics, primary care indicators, etc. Each data stream may provide a single indicator for a disease, but low signal to noise ratio help diseases go unnoticed. 


Different data sources represent different aspects of a complex system and, taken together, can magnify biologically relevant signals and reveal the underlying pattern of a disease.

Traditional statistical methods struggle with the level of data integration required to robustly model complex biochemical systems, whereas the flexibility of deep learning methods cater to multi-modal analysis and help you generate meaningful inferences.


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Automate the analysis of datasets

Automate diagnostic solutions

Proactive detection and live monitoring

Image Analytics

Computer vision can be trained to see what field experts see, but can process at super human speed

Breakthroughs in computer vision image processing occur almost weekly and can be used to flag potential risks to practitioners.

Inattentional blindness leads humans to miss things they're not looking for, whereas computational systems suffer no such bias. People have a tendency to search for what they are already looking for, missing what they don't expect to see.

medical image analytics.jpg

Detect disease through image processing 

Automate early diagnosis accurately

Alert professionals to examine red flags

Two categories of robotic process automation

Patient Engagement

More efficient patient engagement

Introduce automation at every patient touchpoint to attract the right patients and ensure their treatment experience is accurate and streamlined. 

Patient Engagement


Visit our e-commerce and marketing page to see how you can employ machine learning in marketing.


Attracting the right patients using state of the art automation and optimisation techniques.

Image by Stephen Phillips -

Target the right people, optimise marketing efforts


Detailed customer profiling and customer sentiment analysis, as well as general analytics and automation capabilities can be employed.


Details can found in our Business Intelligence section.   

Image by Estée Janssens

Automate scheduling and patient sentiment analysis

Care plans

Intelligently generate patient care plans with software the makes suggestions based on patient behaviour and history. 

Automate check ups with email and scheduling integrations. 

Explore our business intelligence vertical for more.

Image by Nguyễn Hiệp

Design patient care plans

Visit business intelligence

Hospital Admin

Streamline administrative processes

Reduce administrative workloads of highly qualified medical professionals and streamline administrative staff workflows.

Hospital Admin
Image by Martha Dominguez de Gouveia

Optimise hospital admin

Hospital Management

  • Digitise customer data with computer vision and scanning technology

  • Manage bed inventory more efficiently; automatically trigger supply orders

  • Optimise resource utilization to save rental costs and streamline staff rosters through predictive demand forecasts

  • Natural language processing (NLP) can process medical documents, update medical records and schedule patient visits

  • Prioritise patients in real-time and reduce waiting times

  • Automatically update the patient’s Admission-Discharge-Transfer status and simultaneously trigger staff direction


Automate menial processes

Administrative RPA

Employ robotic process automation (RPA) to assist administrative workflows

  • Automate insurance claim handling, ensuring regulatory compliance and avoid manual input errors whilst increasing claim speed

  • Streamline account settling with data merging, consolidation and intelligent calculation, automatically sending invoices or customer alerts

  • Generate and send discharge instructions, informing patients of guidelines and checking in on patients at various stages after/during treatment, with prescription reminders , upcoming appointment notification and tests

Use Cases


Real Client Examples From the Collective

Automated analysis of large proteomic and genomic datasets


A recommendation tool for consumers to improve eating habits

Confidential (scale up)

Medical facility management

Confidential (Government)

Track food consumption and profile users for weight loss advise

Customer Success Consulting LLC, US

Automate medical device reviews to increase regulatory speed

Confidential (regulatory organisation)

Remote health monitoring system for COPD patients.

Confidential (healthcare equipment)

Prostate cancer diagnostics

London Clinic

Multi-specialty digital medical assistant (covid response)


Detect fraudulent resale of pharmaceutical drugs


Dynamic pricing for pharmaceutical products


Key Opinion Leader (KOL) mapping


Domestic abuse and online harassment analysis during Covid-19

Red Dot Foundation

Pre-diagnostic retinal scan for disease recognition


Predict the efficacy of a researching fund, through the analysis of vaccine impact


Medical facility management


Automate patient waiting lists


Liver disease diagnostics

Manchester University

ARA - automated review assitant


Predicting Employee Attrition

UK National Health Service

Predicting clinical trial complexity

Boehringer Ingelheim

Clinical trial optimisation


Skills analytics


Clustering and topic detection on factory error reports

Boehringer Ingelheim

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