In the ever-evolving landscape of healthcare, the integration of Artificial Intelligence (AI) tools has emerged as a transformative force, revolutionizing the way we approach diagnostics, treatment, and patient care.
This blog post sheds light on 16 pioneering AI companies in healthcare, each contributing innovative AI tools that are reshaping the industry. From diagnostic support systems to personalized treatment recommendations, these AI tools in healthcare are not only streamlining processes but also enhancing the precision and efficiency of medical practices.
Explore the latest advancements in AI tools for healthcare, discovering how these innovative companies are changing the way we think about the future of medicine.
16 AI Tools/Companies in Healthcare:
Microsoft Fabric:
Microsoft recently announced that they are developing new healthcare data and artificial intelligence tools within their Microsoft Fabric app, a new data analytics platform.
The new tool can collect data from sources such as electronic health records, images, lab systems, medical devices, and claims systems so organizations can standardize it and access it in the same place, ultimately eliminating the time-consuming task of searching through sources one by one.
Azure AI Health Bot (Microsoft):
Microsoft is adding new healthcare specific tool within Azur AI like Azur AI Health Bot.
Azure AI Health Bot is a cloud platform that empowers developers in Healthcare organizations to build and deploy compliant, AI-powered virtual health assistants.
The bot can be used to pull information from a health organization’s internal data and other reputable external sources.
It can be used by staff to ask questions about specific diseases and internal protocols.
Text Analytics for Health – Azur AI (Microsoft):
Text Analytics for Health is a cloud-based API service within Azur AI that applies machine-learning intelligence to extract relevant medical information from a variety of unstructured texts and data such as doctor’s notes, discharge summaries, clinical documents, and electronic health records and can label them under four key functions named entity recognition, relation extraction, entity linking, and assertion detection.
Patient Timeline model (Microsoft):
Patient Timeline model uses generative AI to extract key events from unstructured data, such as medications, diagnosis, and procedures and organizes them chronologically to give clinicians a more accurate view of a patient’s medical history to better inform care plans.
Clinical Report Simplification model (Microsoft):
Clinical report simplification uses generative AI to allow clinicians to take medical jargon and convert it into simple language while preserving the full essence of the clinical information so that it can be shared with others, including patients.
Radiology insights model (Microsoft):
Radiology insights provide quality checks through feedback on errors and inconsistencies. The model also identifies follow-up recommendations and clinical findings within clinical documentation with measurements (sizes) documented by the radiologist.
Nuance (Microsoft):
Nuance, acquired by Microsoft. It is a conversational AI and cloud-based ambient clinical intelligence for healthcare providers.
It comes with solutions like Dragon Ambient eXperience, Dragon Medical One, and PowerScribe One for radiology reporting, all clinical speech recognition SaaS products built on Microsoft Azure.
Nuance’s solutions work with core healthcare systems, including longstanding relationships with Electronic Health Records (EHRs), which play a big role in alleviating the burden of clinical documentation and helping deliver better patient experiences.
AplhaFold (Google/Deepmind):
AlphaFold is an AI program developed by Google and Deepmind that can predict the shape of a protein, almost instantly, down to atomic accuracy, and was recognized as a solution to the grand challenge of protein-folding by CASP (Critical Assessment of Protein Structure Prediction), a community for researchers to share progress on their predictions against real experimental data.
It was trained on the sequences and structures of around 100,000 known proteins
AlphaFold predictions are available to any scientific community for free.
Nvidia AI
NVIDIA has been involved in the healthcare sector, focusing on leveraging AI for various applications like Medical Imaging, Drug Discovery, Genomic analysis, and Medical Devices.
NVIDIA’s AI technologies, particularly their GPUs (Graphics Processing Units), are well-suited for handling complex computational tasks, making them valuable in healthcare applications. Here are a few areas where NVIDIA has been making strides.
Wysa:
Wysa provides AI coach, an artificial intelligence-based ’emotionally intelligent’ service that responds to your expressed emotions by using evidence-based cognitive-behavioral techniques (CBT), DBT, meditation, breathing, yoga, motivational interviewing, and micro-actions to help you build mental resilience skills and feel better.
The platform also has human mental health trained to listen and support you. Together with AI and human mental health professionals, Wysa aims to support its customers in achieving their goals.
Elemental Cognition:
Elemental Cognition can help pharmaceutical companies find secondary indication data by reliably analyzing and connecting the dots across all the documented evidence with Cora – Collaborative Research Assistant.
EC claims that Cora can accelerate pre-clinical drug discovery by 10-100x by assisting companies with drug repurposing, supporting the researcher’s thought process, automating solution exploration, finding documented evidence that supports the results, and summarizing the results.
Deep Genomics:
Revolutions in AI, RNA biology, and automation are enabling a new approach
to drug development. Deep Genomics is at the forefront.
Deep Genomics aims to create medicine in a similar way we code computer programs.
The company is focusing on the development of steric-blocking oligonucleotides (SBOs) that target the genetic determinants of disease at the level of RNA or DNA. These genetic diseases are mediated by altered molecular phenotypes, such as transcription, splicing, translation, and protein binding. Predicting those alterations is our core competency. The oligonucleotide therapeutic design space includes tens of billions of compounds, but Deep Genomic’s platform makes it possible to search this space efficiently.
Insitro:
Instro uses machine learning (ML) and generative to decode the complexities of biology and unlock transformative new medicines.
Instro generates multi-modal phenotypic cellular data in our automated laboratories and aggregates clinical data from human cohorts. This data is used to fuel machine learning and generative AI to build and interrogate phenotypic models of disease states.
They leverage human genetics to identify causal intervention points in disease and turn them into effective therapeutic interventions in the right patients.
Cleerly Health:
Cleerly is a digital healthcare company transforming the way clinicians approach the treatment of heart disease. Their clinically proven, AI-based digital care platform works with coronary computed tomography angiography (CCTA) imaging to help clinicians precisely identify and define atherosclerosis earlier, so they can provide personalized, life-saving treatment plans for all patients throughout their care continuum.
Ada Health:
Ada AI simplifies healthcare journeys and helps people take care of themselves.
Doctors have trained Ada for years to assess patient’s symptoms in minutes.
Users can check their symptoms online anytime and find out possible causes. You answer some health and symptoms-related questions.
Ada app assesses your answers against its medical dictionary of thousands of disorders and medical conditions.
You receive a personalized assessment report that tells you what may be wrong and what you could do next.
Osmo:
Led by Alex Wiltschko, a former research scientist at Google Brain, Osmo is working towards the digitalization of smell.
The company’s vision is to improve the health and well-being of human life through the digitalization of smell.
Once achieved, this technology can help us detect diseases earlier, track pandemics faster, grow more food, catch food spoilage before it harms, ward off insects, and much more.
As a startup, Osmo will be focusing on the Flavour and Fragrance industry, responsible for nearly every smell in every product we experience including snacks, perfumes, and laundry detergents.
The problem is that many of the ingredients used by these companies are either not healthy for humans or the environment and this is where Osmo aims to step in and provide a better solution.