6 Most Promising Healthcare Startups that Can Leverage ML in Their Processes

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Grzegorz Mrukwa

Updated Feb 21, 2024 • 6 min read

The AI-powered solutions have a massive potential in healthcare.

Computers equipped with software based on convolutional neural networks are better at detecting skin cancer than experienced dermatologists, and Deep Learning beats radiologists in diagnosing pneumonia.

Machine Learning will have a special place in the healthcare industry. Yet many companies are still reluctant to explore AI-based solutions potential for various reasons: financial constraints, legal issues, or simply they are not aware of the possible uses. Today, we take a look at six very promising healthcare startups and try to find a few ways they could leverage Machine Learning to gain a competitive advantage on the market.

1. Radpoint

Radpoint is a leading provider of cloud-based technologies and services for radiology. The company develops fully cloud-based PACS/VNA system, which stores medical images captured via Computer Tomography, Magnetic Resonance Imaging, etc. Their solution is fully based on cloud computing (AWS), which is unique for medical image processing solution. Thanks to the cloud the system has great scaling and computing capabilities.

Possible Machine Learning solutions that RadPoint could take advantage of:

  • segmentation of lesions within tissues (with tomography or resonance);

  • estimating calcification of coronary arteries (mostly tomography);

  • intra-operative support systems (which provide additional vision e.g. with ultrasound imaging);

  • detection of pneumonia or cardiovascular diseases.

2. Medical Simulation Technologies

Medical Simulation Technologies is a company that introduced an innovative simulation system for TEE examination ("Mr. TEEmothy") and micro-invasive heart procedures based on CT scans from real patients for the practical training of medical professionals and future medical doctors. Its promise is that it will increase the potential number of good quality diagnostic studies, at the same time preventing patients and physicians from unnecessary discomfort and the possible complications of real-life examinations.

Possible Machine Learning solutions that Medical Simulation Technologies could take advantage of:

  • improving simulations with automated tutoring;

  • creating automated instructions for new users, based on the information collected from the sensors about how an experienced doctor carries out an investigation.

3. Embleema

Embleema is a Blockchain healthcare startup, that allows users to earn by sharing their medical data. Their mission is to solve the common problems with digital health data, such as misinformed diagnosis and suboptimal care, sharing and monetization without patients’ knowledge, poor assessment of drug efficacy and safety, and, finally, the limited use of clinical research and treatment improvements.

Possible Machine Learning solutions that Embleema could take advantage of:

  • providing additional functionality to users and companies interested in the data thanks to the analysis of internal data;

  • leveraging the information about medical records to extend insights that are directly available to users and to provide them with some suggestions (e.g. about doctors);

  • using unsupervised natural language processing to provide automated record summaries.

4. Kry

Kry is an application that connects potential patients with qualified medical doctors and enables them to schedule an online appointment at a convenient time and place. Thanks to digital technology, KRY provides equitable access to high-quality health care at the patient’s fingertips. Over the last four years of operation, the company has helped over half a million patients across Europe. The major challenge here is that the app doesn’t store data. Therefore implementing ML would be a much harder task, yet still possible, keeping the highest privacy standards.

Possible Machine Learning solutions that Kry could take advantage of:

  • parsing symptoms to automatically discover potential risks of a disease;

  • qualifying the disease severity based on the patients’ data.

5. Lifesum

Lifesum is an application that helps people achieve goals such as losing weight, building muscles, eating healthily, etc.). For a low monthly fee, the user gets a comprehensive set of tools and features for improving their lifestyle. The app enables them to count calories, get personalized diets, track habits, and analyze body composition.

Possible Machine Learning solutions that Lifesum could take advantage of:

  • automating diet recommendations based on users’ behavior and data;

  • providing more personalized meal recommendations based on feedback;

  • improving nutrition plans based on diet effectiveness.

6. Oxford Heartbeat

Oxford Heartbeat is a London-based startup working on medical device software to make planning for surgeries simpler, faster, and more accurate. The system uses non-invasive modelling of the human body in order to enable surgeons to visualize crucial information in preparation for surgery. The company has already leveraged the power of predictive computations, big data and AI, to provide a unique solution.

Some more Machine Learning solutions that Oxford Heartbeat could take advantage of:

  • estimating the calcification of coronary arteries thanks to more data processing,

  • having blood vessels mapped, detecting aneurysm or atherosclerosis.

There are more examples of companies we could mention here, and, definitely, more possible solutions we could implement in their systems to streamline processes and provide better and more customized products for their users. If you want to discuss how your company can take advantage of Artificial Intelligence, drop us a message.

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Grzegorz Mrukwa

Former Data Science Manager at Netguru

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