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Dr. Bertalan Mesko, PhD
21 min | 13 August 2024
Read about the biggest artificial intelligence companies in healthcare ranging from start-ups to tech giants to keep an eye on in the future.
Key Takeaways
The medical AI field is rapidly evolving with a mix of startups and tech giants, all set out to revolutionize healthcare
As AI continues to demonstrate its efficacy in healthcare, such as through improved diagnostic accuracy, the industry faces the challenge of integrating these advancements in a way that is safe, accessible and fair to all patients
The first healthcare facets where AI will have a profound impact will likely be administration, medical imaging, and drug development
The field of medical AI is buzzing. More and more companies set the purpose to disrupt healthcare with the help of artificial intelligence. Given how fast these companies come and go, it can prove to be hard to stay up-to-date with the most promising ones. Here, I collected the biggest names currently on the market ranging from start-ups to tech giants to keep an eye on in the future.
To further help you keep up with what AI brings to medicine, The Medical Futurist team made just about that. I highly encourage you to read it and would love to hear your thoughts!
Artificial Intelligence has to and will redesign healthcare
No one doubts that artificial intelligence has unimaginable potential. Within the next couple of years, it will revolutionise every area of our life, including medicine. However, many have their fears and doubts about AI taking over the world, Stephen Hawking even said that . Nevertheless, I am fully convinced that if humanity prepares appropriately for the AI age, artificial intelligence will prove to be the next successful area of cooperation between humans and machines.
Concerning healthcare, – and for the better. AI could help medical professionals in designing treatment plans and finding the best-suited methods for every patient. It might assist repetitive, monotonous jobs, so physicians and nurses can concentrate on their actual jobs instead of e.g. fighting with the treadwheel of bureaucracy.
AI will assist medical professionals and patients in many ways
In fact, our aims to prepare healthcare and medical professionals for the era of human-machine collaboration and is a great way to know more about how such a collaboration can be beneficial to medicine.
Mining medical records is the most obvious application of AI in medicine. Collecting, storing, normalising, tracing its lineage – it is the first step in revolutionising existing healthcare systems.
Look at this example which shows that AI is awfully needed
Just look at this picture taken a few years ago in a Hungarian hospital in the capital city, Budapest. The personnel manages patients’ appointments MANUALLY on a giant blackboard, and I do not even want to comment on the index card holder. The hospital staff told the reporter that their online system can’t deal with multiple appointments for the same patient, so to be on the safe side, they still use the board on the wall.
[image]https://cdn.medicalfuturist.com/wp-content/uploads/2017/01/state-of-affairs-in-hu-hospital-768x576.jpeg[/image]
State of Affairs in a Hungarian Hospital – Photo Credit: Tamas Meszaros/Index
It is obvious that such systems are unsustainable and artificial intelligence could offer help. And some entrepreneurs already realized the huge transformative as well as financial potential of medical AI.
The global AI in healthcare market size was $19.54 billion in 2023 and is expected to reach $490 billion by 2032. The market is truly booming, hence start-ups grow out of nowhere like mushrooms. So, let me introduce you to companies that are on the best way to democratize healthcare through artificial intelligence. It is truly worth keeping an eye on them since they are great partners in building more transparent and effective healthcare.
Mining Medical Records within minutes
In the age of Big Data, there is no question about how valuable patient data is. When such tech giants as Google or IBM appear in the field of patient data mining, everyone knows it is something worth doing.
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In 2019 DeepMind’s health team to “build products that support care teams and improve patient outcomes.” Google Health is tapping into AI’s potential to help in and , predicting , , and [url=https://ai.googleblog.com/search/label/Health]more{/url].
Source: https://www.neowin.net/
Also, , the life sciences arm of Google’s umbrella corporation, Alphabet, , . It aims to use some of the same algorithms that power Google’s famous search button in order to analyse what makes people healthy.
Automating Medical Administration
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Medical documentation specialist Augmedix developed solutions to extract data from natural physician-patient conversations and convert it to medical notes in real-time, which are automatically transferred to EHR systems of healthcare providers. Addressing the administrative burdens of healthcare workers and automating these tasks will become a major time saver in the sector, and potentially also a direction which can help reduce burnout symptoms of medical personnel.
Their latest announcement was the launch of a fully automated, generative for Emergency Departments. This builds on their , a clinician-controlled mobile app that uses generative AI to instantaneously create a fully automated draft medical note after each patient visit.
The company published showing “that clinicians in ambulatory care settings save up to one hour or more per clinic day” by using their platform.
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Their AI-powered platform uses ambient voice technology and natural language processing to automate the creation of medical notes. By using advanced speech recognition models, DeepScribe captures natural conversations between physicians and patients, transforming them into comprehensive, accurate clinical notes. The EHR integration means physicians can quickly review and sign off the notes. According to the company, their solution saves doctors up to .
DeepSribe says they have built their tool using the world’s largest database of natural patient conversations, and state that it is significantly (32%) more accurate than GPT4. (Which is of course great, but we have to note that GPT was never specifically trained for medical purposes).
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Nabla is a company that focuses on developing AI-powered tools for healthcare professionals, with a particular emphasis on medical documentation. Their platform utilizes natural language processing (NLP) and machine learning to streamline clinical workflows, enhance patient communication, and improve the overall quality of care.
Nabla’s AI Copilot automatically drafts medical notes based on clinician-patient conversations, saving valuable time and reducing the risk of errors. Their AI-powered chatbots can also handle routine patient inquiries, freeing up clinicians for more complex tasks. Additionally, Nabla offers an AI-driven knowledge base that provides clinicians with instant access to relevant medical information, supporting informed decision-making at the point of care.
Disrupting Medical Imaging
Medical imaging encompasses every technique and method with which it becomes possible to represent the inner secrets of the body. X-ray, ECG, MRI, ultrasound, tomography – to name a few of the most commonly known ones. And what comes to your mind when you think about these procedures? Usually a huge, unfriendly room in a hospital with an even bigger, expensive-looking, and complicated machine.
And if you think that, then you are awfully right. Also, exactly because current technologies are unwieldy, expensive, and require extensive training. This is exactly what the following innovative AI start-ups want to change.
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Jonathan Rothberg established his start-up, Butterfly Network in 2011 with the goal to create a new that can . His ultimate aim is to automate much of the medical imaging process.
: Butterfly iQ Clinical Image (YouTube)
The company’s Butterfly iQ was the first step towards this goal. This portable handheld device uses an Ultrasound-on-Chip technology to replace the traditional transducer system with a single silicon chip, emulating any type of transducer (linear, curved or phased) allowing for whole-body imaging from a single probe. By combining semiconductors, artificial intelligence, and cloud technology in a pocketable form, the Butterfly iQ is making remote medical imaging a reality, a boon to remote communities, some of which are benefiting from such crucial medical information .
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Enlitic uses the power of deep learning technologies, specifically its prowess at certain forms of image recognition to harvest the data stemming from radiology images and apply it in unique medical cases. Deep learning actually means the process by which a computer takes in data and then, based on its extensive knowledge drawn from analysing other data, interprets that information.
The start-up’s technology can interpret a medical image in milliseconds —up to 10,000 times faster than the average radiologist. It integrates seamlessly into any existing health system so as to optimise patient outcomes while empowering physicians. A study even showed that with Enlitic’s help, radiologists % faster. In , the algorithm had even been able to detect malignant lung nodules up to 18 months before a biopsy was even ordered.
. (Youtube)
The company has in August 2022, under the agreement GE will embed Enlitic’s into GE’s PACS software to improve radiologist workflow and efficiency. The collaboration is designed to improve data standardisation and improve efficiency by eliminating the need for radiologists to spend time on activities like correcting broken hanging protocols.
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Formerly known as Arterys, this team focuses on where the cloud, artificial intelligence, and medical imaging meet. Originally focused on cloud-based platforms for faster radiology image examination and reduced missed detections, Arterys is now a key component of Tempus Labs’ precision medicine platform.
Their Pixel platform aids radiologists and oncologists by automating image analysis, quantifying lesions, tracking disease progression, and generating detailed reports. These tools integrate seamlessly with existing hospital systems, enhancing workflow efficiency and diagnostic accuracy across various medical fields, including oncology, cardiology, and pulmonology. Tempus Radiology’s AI-driven technology supports precise and timely medical decision-making, ultimately improving patient outcomes.
(Youtube)
When Arterys in October 2022, it was the biggest acquisition in the history of imaging AI, highlighting the segment’s continued shift beyond traditional radiology use cases.
4) GE Verisound AI
Formerly known as Caption Health, which was earlier also known as Bay Lab, the company was acquired by GE Healthcare in 2023. The company’s AI software aims to make it easier for non-specialists to capture high-quality ultrasound images of the heart. This can lead to earlier disease detection and improved patient outcomes, particularly in underserved communities.
2022 was a strong year for the company: they launched the in the US, and following the FDA 510(k) clearance, they also for their AI technology platform to detect cardiac disease.
The latest news from the new, GE-backed era is the launch of Caption AI, a combination of portability and AI guidance. Caption AI provides real-time visual guidance to prompt the user on probe movements and includes a quality meter to ensure the user is getting the best possible images. Once an image is acquired, the AutoEF feature automatically calculates the left ventricular ejection fraction. The solution automatically captures the best-quality image from each view.
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Using their red dot algorithm based on deep learning models, Behold.ai’s software can classify a CXR and localize its findings as heatmaps. Their AI was trained from over 30,000 images, all of which had been reviewed by experienced UK-certified Consultant Radiologists. This resulted in an algorithm with over 90% accuracy that can detect abnormalities within seconds.
behold.ai red dot® (Youtube)
The company, headquartered in London, is already collaborating with the NHS and has deployed its AI solution in several NHS Trusts. A has shown that their solution reduced radiologists’ workload by 29% and reduced the waiting time for a diagnosis by 71%.
In a recent post Chairman and CEO Simon Rasalingham wrote about a new partnership , showing dedication to be part of the upcoming quantum computing revolution – if and when it arrives.
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Lithuanian Oxipit develops automation technologies for medical imaging. The company’s ChestEye is an AI double-reading medical imaging tool. The product analyses final radiologist reports and corresponding medical images. Operating in near-real-time, Oxipit Quality helps to identify reporting errors and improve patient outcomes.
Oxipit and Astra Zeneca launched a in 2021, the software was deployed in two major primary care centers in Lithuania. With nearly 50,000 chest X-ray images analysed, ChestEye Quality identified 190 clinically relevant missed findings, 82 of which were potentially missed subtle pulmonary nodules, potentially identifying additional early instances of lung cancer.
A showed that the ChestEye algorithm identified almost as good as the reference radiologists.
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Clarius develops portable wireless HD ultrasound machines that come with cloud storage and an AI-powered app, which can automatically detect anatomy at the macro level, allowing physicians to scan without having to adjust the system, the algorithm is also used to automatically optimise image quality.
Following the FDA clearance issued in 2021, the company Certifications and for their devices in 2022.
T-Mode™ (Youtube)
The company had a strong year in 2024. Their OB AI fetal biometric measurement tool . Clarius also received the dual-array wireless handheld scanner for whole-body ultrasound imaging, which also got regulatory approval in the UK and Australia. They also launched , which shows an adjacent image with distinctive graphics and text labels that instantly identify one or more anatomical structures for educational purposes.
Speeding up biological data management and drug development from years to weeks
Developing pharmaceuticals through clinical trials sometimes takes more than a decade and costs billions of dollars. Speeding up the process of drug development and making it more cost-effective through AI technologies would have an enormous effect on today’s healthcare.
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This San Francisco-based company aims to reduce the costs of medicine development by using supercomputers to predict in advance from a database of molecular structures which potential medicines will work and which won’t.
Their technology, called , uses convolutional neural networks, an AI technology similar to the one that enables self-driving cars or that allows you to talk to your phone. By taking cues from millions of experimental affinity measurements and thousands of protein structures, AtomNet is able to predict the binding of small molecules to proteins and thereby identify an effective and safe drug candidate.
(Youtube)
Atomwise signed a $1.2 in August 2022, it centers on leveraging the AtomNet platform to research small molecules aimed at up to five drug targets. The platform incorporates deep learning for structure-based drug design, enabling the rapid, AI-powered search of Atomwise’s proprietary library of more than 3 trillion synthesizable compounds.
In a published in 2024, the company reported that researchers applied AtomNet to 318 targets through collaborations with over 250 academic labs across 30 countries, representing the largest and most comprehensive virtual high-throughput screening campaign to date. AtomNet successfully identified structurally novel hits for 235 of the 318 targets evaluated.
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with the purpose of building a proprietary drug discovery platform that combines the best elements of high-throughput biology and automation with the latest advances in AI to date, they’ve imaged and generated over 19 petabytes of biological data to feed their AI.
RxRx1: (Youtube)
By pairing computer vision with classic machine learning and neural networks, the company is able to conduct every week. In this way, Recursion’s algorithm can reveal new drug candidates, mechanisms of action, and potential toxicity, which can lead to novel treatments for patients.
In 2024 they , an extremely powerful supercomputer, to accelerate the discovery of new drugs using advanced AI and vast biological datasets. In an August announcement, they announced the acquisition of Exscientia, a smaller competitor with a pipeline of treatments for immunology and oncology.
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Toronto-based Deep Genomics created an AI platform that works in concert with its experts to discover and develop genetic medicines, including for conditions with high unmet needs. For instance, that it discovered a novel treatment target and corresponding drug candidate for Wilson’s disease. Deep Genomics is also working on , which it describes as a “toolkit for controlling cell biology along crucial pathways”, which will allow for faster discovery of therapies.
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Originating in Hungary, Turbine developed its proprietary Simulated Cell technology, a virtual high-definition tumour cell based on manually curated literature that can be customised into a desired model. Millions of simulated experiments, guided by an AI, can then be run on this model to analyse and better understand the underlying pathomechanism and design the best therapy.
Source: https://turbine.ai/
The technology is already used in collaborations with Bayer, the University of Cambridge, and top Hungarian research groups to find new cancer cures, speed up the time to market, and save the lives of patients suffering from currently incurable forms of the lethal disease. Turbine in 2024 to identify and understand mechanisms of resistance to therapy in hematological cancers.
Health Management
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Ada is a health company based in Berlin that operates an end-user self-assessment app. The app started out as a Platform as a Service (PaaS) for doctors and was adapted in 2016 to focus on the bits patients could understand. The app has almost 14 million users. It takes reported symptoms, matches them with symptoms of patients of similar age and gender, and reports the statistical likelihood that the patient has a certain condition. It is currently available in English, German, Spanish, Portuguese, Swahili, Romanian, and French.
The in Epic App Orchard in May 2022 and in Europe in the fall of 2022.
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MySense is a wellbeing analytics platform. It collects data related to the activities of daily living through passive IoT sensors. Its AI algorithm learns an individual’s behavior patterns to establish what ‘being well’ looks like for that person. The platform allows patients to monitor their health at home and identify declines in health in real time.
An interesting cooperation is between the company and the South Warwickshire NHS Foundation Trust, which aims to help people remain in their homes and retain their independence for longer, and the use of their technology the number of hospital visits.
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Diabeloop is a key player in therapeutic AI applied to insulin delivery. Its solution for automated type 1 diabetes treatment calculates the insulin doses patients need throughout the day and administers them in an automated and personalised manner.
The company of a 12-month data-collecting study in 2022, the cohort included 4,162 patients across Germany, Italy, Spain, The Netherlands, and Switzerland. The study demonstrated time-in-range improvements of 17.6% over one year of real-world use. Patients spent less than 20 minutes per day in hypoglycemia (low blood sugar).
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Skinvision developed an app to remotely evaluate suspicious skin lesions. Users take a picture of the lesion in question, upload it to the app, fill out a short questionnaire and will receive a preliminary evaluation prepared by the AI algorithm in a few minutes. This preliminary diagnosis will be followed by a final evaluation conducted by a human dermatologist in 1-2 days.
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Woebot is an AI-powered mental health app that uses cognitive behavioral therapy tools to support teen and adult users in finding their inner peace. The chat algorithm obviously doesn’t substitute a trained psychologist or psychiatrist, however, many users reported that the app helped them take the first step. They often reflected on the positive aspects of using their phones as mental help: you don’t have to leave your apartment and there is no stigma attached to the “sessions”.
A that the app was able to effectively engage people with depression in empathetic conversations and assist in the treatment of their symptoms.
Care Coordination and Disease Detection
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Viz.ai is a leading player in the application of artificial intelligence to disease detection and care coordination. Their flagship product, Viz.ai One, is an AI-powered platform that rapidly analyzes medical images to identify suspected diseases, such as strokes, aneurysms, and intracranial hemorrhages. This technology significantly accelerates the diagnostic process, enabling faster treatment decisions and potentially improving patient outcomes.
Viz.ai also offers Viz Connect, a communication tool that facilitates seamless collaboration among multidisciplinary care teams. By streamlining workflows and enabling real-time communication, Viz Connect ensures that the right specialists are alerted and involved in the patient’s care as quickly as possible. Viz.ai’s solutions are already deployed in numerous hospitals and health systems, helping to transform the way critical care is delivered.
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Hippocratic AI focuses on developing generative AI “agents” that assist healthcare professionals by handling non-diagnostic tasks through empathetic, conversational interactions. These AI agents, powered by Hippocratic’s healthcare-focused large language model (LLM) and NVIDIA’s technology, have been tested and shown to outperform human nurses in some tasks, such as identifying medication impacts and detecting toxic drug dosages.
Their virtual nurses are capable of handling routine patient calls for checkups, risk assessments, pre-op instructions, and other non-emergency medical interactions. These AI nurses are designed to improve access to care, reduce costs, and free up human clinicians for more complex tasks – and .
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By listening in on patient interviews, analysing the caller’s voice and background noises, and taking insights from historical data and artificial neural networks, the AI is able to understand the context and patterns in critical conversations. In this way, it can assist emergency medical professionals by alerting them if, say it identifies a heart attack in progress and making life-saving decisions. Their AI assists dispatchers through the triaging flow, but also listens in to the interview and makes suggestions, like a real-time second opinion.
(Youtube)
Dispatchers in Denmark, where the company is based, can identify a heart attack from descriptions over the phone . On the other hand, a study with Corti showed that the AI performed better than emergency medical dispatchers in identifying cardiac arrest in emergency phone calls.
If you know about other companies disrupting healthcare through artificial intelligence, !
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Top AI Companies In Healthcare To Keep An Eye On [re: Mod vege]
(старо куче)
