5 Top Machine Learning Startups Impacting The Healthcare Industry
Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the healthcare sector. As there are a lot of such startups working on various different applications, we want to share our insights with you. Today, we take a look at 5 promising machine learning startups.
Heat Map: 5 Top Machine Learning Startups
For our 5 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 690 relevant solutions. Depending on your specific needs, your top picks might look entirely different.
Which startups develop the other 685 solutions?
Alixir – Identifying Diseases & Diagnosis
One of the main machine learning applications in healthcare is the identification and diagnosis of diseases that are hard to detect. Machine learning models that predict disease susceptibility also aid in the early diagnosis of illnesses such as cancer, genetic diseases, and more. Moreover, a deep learning-based prediction model is able to identify patterns that are not obvious to or even observable by human clinicians.
Australian Alixir uses deep learning and computer vision technology to detect cancerous changes and calcification on a routine screening mammogram. The system segments a digital mammogram and provides customized reports in a few seconds. It makes the screening process more accurate and leads to earlier detection and thus better patient outcomes.
Reverie Labs – Drug Discovery & Development
Machine learning is applied at all stages of new drug discovery including designing the chemical/protein structure of drugs, target validation, investigating drug safety and managing clinical trials. Deep learning software is used to sift through millions of possible molecules in a short time period, and then analyze simulations to see how the medicine will behave in the human body. This makes the drug discovery process faster, more precise and cost-effective for pharmaceutical companies.
The US-based startup Reverie Labs accelerates preclinical drug development by applying machine learning to lead generation and optimization processes. The platform analyzes early concepts for molecules from pharmaceutical scientists and suggests possible improvements to shorten the amount of time it takes to reach clinical trials. Currently, the company is working on drug research for diseases such as influenza and cancer.
Cancer Center – Medical Imaging Diagnosis
Medical images are one of the largest data sources in the healthcare industry. Machine learning algorithms process massive amounts of these images including computerized tomography (CT) or magnetic resonance imaging (MRI) scans at rapid speed and are extremely precise in identifying even the smallest anomalies. Also referred to as Computer Vision, these algorithms analyze various medical images and reports and diagnose malignancies or abnormalities with a higher accuracy rate than healthcare professionals.
The Poland-based Cancer Center builds a specialized algorithmic solution to analyze and share medical images for oncology and radiology. Through deep learning and machine learning technologies, the platform analyzes medical images and provides a diagnosis that supports people and physicians to get a second opinion about their image data and pre-evaluate the diagnosis before the actual is made. The system also automatically segments images, generates statistical descriptions, counts cells, mitosis, and recognizes the type of cells.
OncoHost – Personalized Medicine
Medicine is becoming more individualized with access to personal patient information in the form of data from wearable devices, electronic medical history, genetic information, lifestyle data, etc. By applying artificial intelligence (AI) and machine learning to this data, researchers develop a few treatment options based on a patient’s symptomatic history and available genetic information. For example, machine learning can predict the risk of cancer recurrence in each patient by identifying special biomarkers. This helps personalize treatment by allowing patients with a low risk of cancer recurrence to receive less aggressive treatment.
OncoHost, a startup based in Israel, develops personalized strategies to maximize patients’ response to cancer therapy using advanced machine learning technology. Utilizing proprietary proteomic analysis, the company explores early identification of non-responsiveness to cancer treatments and the discovery of new targets to overcome treatment resistance.
MedInReal – Smart Health Records
Maintaining health records is an obligatory process that takes a lot of clinician’s time to complete. Machine learning helps to ease these processes while saving time, effort, and money. By spending less time maintaining electronic health records (EHRs), physicians spend more time with their patients. By leveraging Natural Language Processing (NLP) tools that use algorithms to identify and categorize words and phrases, physicians can additionally dictate notes directly to EHRs during patient visits.
The Dutch startup MedInReal provides an AI-based virtual care assistant for doctors, that automates repetitive tasks, updates EHRs and integrates with other existing tools. Its intelligent capabilities allow the assistant to identify structured data elements, ensuring a match with medical terminology. The system also provides advanced analytics to find hidden patterns on treatment plans & medications by using medical notes with NLP software.
What About The Other 685 Solutions?
While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence for your Proof of Concept (PoC), partnership, or investment targets. The 5 machine learning startups showcased above are promising examples out of 690 we analyzed for this article. To identify the most relevant solutions based on your specific criteria and collaboration strategy, get in touch.
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