How Many Hospitals Use Artificial Intelligence? (New Data)

In a recent poll conducted by Morning Consult, insights from 2,200 adults were gathered, with a margin of error of ±2%. The key findings are as follows:

1. Positive Perception

A significant 65% of respondents who possess knowledge about AI's role in healthcare believe that it will predominantly have a positive impact on the field.

2. Acceptance of AI Roles 

Approximately 58% of those surveyed expressed comfort with AI being utilized for disease detection support, while 57% were open to AI assisting in administrative tasks within healthcare settings.

3. AI in Disease Treatment and Diagnosis

More than half of the respondents (53%) indicated comfort with the use of AI in disease treatment support, and 52% were also comfortable with AI contributing to disease diagnosis support.

4. Expectations of Improvement

A notable 61% of participants are optimistic that AI will enhance diagnosis times, 57% believe it will increase accuracy, and 54% anticipate improved patient outcomes as a result of AI integration in healthcare.

Health care spending on AI software is expected to grow 40% in 2023, from $4.4 billion in 2022 to nearly $6.2 billion in the new year, according to Omdia forecasts.

However, the poll also uncovered certain reservations among Americans regarding AI in healthcare.

These concerns present areas of focus for medtech companies and policymakers as they navigate the implementation of AI in healthcare systems.

The reservations extend to AI's involvement in robotic surgery, primary care, virtual nursing assistants, and specialty care, suggesting the need for thoughtful consideration and transparent communication as AI continues to shape the future of healthcare.

Also read; Medical Errors Facts & Statistics in Healthcare

Challenges and Considerations for AI Adoption

As healthcare organizations increasingly embrace the potential of artificial intelligence (AI) in healthcare, they encounter a unique set of challenges that require careful consideration.

These challenges encompass ethical and regulatory aspects specific to the healthcare domain.

Among the most critical challenges are:

1. Data Privacy and Security

AI systems in healthcare often handle vast amounts of personal health information. Safeguarding this data is paramount to prevent misuse. Robust data privacy measures must be implemented to protect patients' sensitive information from potential exploitation.

2. Patient Safety and Accuracy

AI systems must be meticulously trained to recognize intricate patterns in medical data, understand the complex relationships between various diagnoses and treatments, and provide highly accurate recommendations tailored to each individual patient. Ensuring the safety and well-being of patients is a top priority.

3. Algorithm Training

Training AI algorithms in healthcare is a complex task. It involves teaching the system to make sense of intricate medical data, which can be challenging due to the variability and complexity of healthcare information.

4. Integration with Existing IT Systems

Integrating AI seamlessly with existing healthcare IT systems can pose significant challenges. A deep understanding of existing technology is essential to ensure smooth interoperability and minimize disruption to medical providers.

5. Physician Acceptance and Trust

For AI to thrive in healthcare, it must gain the acceptance and trust of medical professionals. Physicians need to have confidence that AI systems provide reliable advice and adhere to the latest medical research. Transparency in AI decision-making processes is crucial to build this trust.

6. Regulatory Compliance

Adherence to federal regulations is paramount to ensure ethical AI usage in healthcare. Stricter guidelines and regulations are essential to safeguard patient safety and prevent AI from compromising healthcare standards.

Healthcare AI Players

1) Google Health/DeepMind

In 2019, DeepMind's healthcare team joined forces with Google Health with a shared vision of creating products that support healthcare teams and enhance patient outcomes. Google Health has been actively harnessing the potential of artificial intelligence (AI) in various healthcare applications, including breast cancer screening, outcome prediction, and preventing vision impairment.

What sets Google Health's efforts apart is their tangible results. Collaborating with DeepMind, they developed an AI-powered solution for the detection of breast cancer. Remarkably, this algorithm demonstrated exceptional performance by surpassing the diagnostic accuracy of human radiologists. On average, it outperformed them by an impressive margin of 11.5%.

This accomplishment highlights the practical impact AI can have in healthcare. It signifies that AI technologies have the potential not only to match but also to exceed human capabilities in critical medical tasks like early disease detection. This progress is a testament to the significant strides being made in the integration of AI into healthcare, promising improved patient care and better health outcomes in the future.

 Augmedix

Augmedix, a medical documentation specialist, has developed innovative solutions to streamline the process of extracting vital information from natural conversations between physicians and patients. This technology instantly converts these interactions into real-time medical notes, which are automatically integrated into the Electronic Health Record (EHR) systems used by healthcare providers.

The significance of Augmedix's work lies in its ability to address the administrative burdens that often weigh down healthcare professionals. By automating these documentation tasks, it not only saves valuable time but also has the potential to mitigate burnout symptoms experienced by medical personnel.

Their recent announcement introduces "Chart prep," a new service equipped with technology that prepares a structured patient note and relevant content for physicians. This preparation is tailored to the specific type of visit and the patient's previous medical records. As a result, physicians can efficiently review this information before meeting with the patient. The data gathered includes patient demographics, medication changes, current medical history, imaging and lab results, immunization records, as well as past medical, family, and social history.

This advancement marks a significant step toward optimizing healthcare workflows by reducing manual documentation tasks, allowing physicians to focus more on patient care and less on administrative duties. It underscores the potential of technology to enhance the efficiency and effectiveness of healthcare delivery while alleviating the strain on medical professionals.

Butterfly Network

In 2011, Jonathan Rothberg founded his startup, Butterfly Network, with an ambitious mission: to create an innovative handheld medical imaging device that could revolutionize the accessibility and cost-effectiveness of MRI and ultrasounds while also automating aspects of the medical imaging process.

The flagship product of Butterfly Network, the Butterfly iQ, represents a significant step towards achieving this vision. This portable handheld device leverages cutting-edge Ultrasound-on-Chip technology, a breakthrough that replaces the traditional transducer system with a single silicon chip. What's remarkable is that this single chip can emulate various types of transducers, including linear, curved, or phased ones. This innovation enables comprehensive whole-body imaging using just one probe.

By integrating semiconductors, artificial intelligence, and cloud technology into a pocket-sized device, the Butterfly iQ has transformed the landscape of remote medical imaging. It has made it possible for medical professionals to conduct imaging procedures remotely, which is particularly beneficial for underserved and remote communities. For some of these communities, access to such crucial medical information has become a reality for the first time, marking a significant advancement in healthcare accessibility and technology's role in bridging gaps in medical services.

Arterys

Arterys, a pioneering startup, operates at the intersection of cloud technology, artificial intelligence, and medical imaging, aiming to revolutionize the field by bringing the power of the cloud to medical imaging.

Their cutting-edge platform, which has received FDA clearance, harnesses the capabilities of deep learning AI. It is designed to expedite the process of radiology image analysis, significantly reducing the likelihood of missed detections by as much as 70%. Additionally, this technology automates routine tasks, thus freeing up valuable time for physicians. This newfound time can then be redirected towards delivering high-quality patient care, enhancing overall healthcare services.

What sets Arterys apart is its continuous accessibility as a 24/7, always-connected solution. This feature enables users to access critical patient data from any location worldwide, at any time. This global accessibility contributes to more efficient and effective healthcare delivery.

In a significant development, Arterys was acquired by Tempus Labs, a prominent player in precision medicine AI, in October 2022. This acquisition marks a milestone in the realm of imaging AI, underscoring the expanding applications of this technology beyond traditional radiology use cases. It signals the ongoing transformation of medical imaging and its increasing integration with advanced AI solutions.

Behold.ai 

Behold.ai has developed an advanced software solution employing a deep learning model-based red dot algorithm. This innovative technology is capable of classifying chest X-rays (CXR) and pinpointing specific findings through the use of heatmaps. The AI system underwent rigorous training using a dataset of over 30,000 images, each meticulously reviewed by experienced UK-certified Consultant Radiologists. This extensive training process has resulted in an algorithm that achieves remarkable accuracy, exceeding 90%. Notably, this AI can swiftly detect abnormalities within a matter of seconds, showcasing its potential to expedite and enhance the diagnostic process in healthcare.

Atomwise

Based in San Francisco, Atomwise has a mission to revolutionize the field of medicine development by leveraging supercomputers to predict, in advance, the effectiveness of potential medicines. Their cutting-edge technology, known as AtomNet, harnesses the power of convolutional neural networks, an AI technology akin to what drives self-driving cars and enables voice recognition on your phone.

AtomNet's capabilities are derived from an extensive database containing molecular structures. It draws insights from millions of experimental affinity measurements and thousands of protein structures. By doing so, AtomNet can forecast how small molecules bind to proteins, thus identifying promising and safe drug candidates.

Notably, Atomwise entered into a substantial $1.2 billion research collaboration with Sanofi in August 2022. This partnership focuses on utilizing the AtomNet platform to explore small molecules targeting up to five specific drug targets. The platform's incorporation of deep learning for structure-based drug design empowers it to conduct rapid AI-driven searches within Atomwise's vast proprietary library, which contains over 3 trillion synthesizable compounds. This innovative approach has the potential to significantly reduce the costs and time associated with drug development, opening doors to more efficient and effective medicines in the future.

Recursion Pharmaceuticals

Established in 2013, Recursion is a pioneering drug discovery company that embarked on a mission to develop a proprietary drug discovery platform. This platform seamlessly integrates high-throughput biology and automation with cutting-edge AI technologies. Notably, the company has achieved a remarkable milestone by imaging tens of billions of human cells and accumulating an extensive dataset exceeding 19 petabytes of biological data to fuel their AI-driven endeavors.

Recursion's distinctive approach combines computer vision with classical machine learning techniques and neural networks. This synergy enables the company to execute approximately 2 million experiments each week. Through this rigorous process, Recursion's algorithm has the capability to unveil new drug candidates, elucidate mechanisms of action, and identify potential toxicity concerns. These insights have the potential to pave the way for the development of innovative and effective treatments for patients, highlighting the transformative power of AI in the realm of drug discovery.

Diabeloop

Diabeloop stands as a significant player in the realm of therapeutic AI, specifically in the context of insulin delivery for managing type 1 diabetes. Their innovative solution is designed to automate and personalize insulin treatment, determining the precise insulin doses required by patients throughout the day.

In 2022, the company unveiled the outcomes of an extensive 12-month data collection study involving 4,162 patients distributed across Germany, Italy, Spain, The Netherlands, and Switzerland. This study showcased impressive results, indicating a noteworthy improvement in "time-in-range" metrics by 17.6% over the course of one year in real-world settings. Equally important, patients experienced less than 20 minutes per day in hypoglycemia, addressing a critical concern associated with low blood sugar levels. These findings underscore the potential of AI-driven solutions like Diabeloop to enhance the management and quality of life for individuals living with type 1 diabetes.

Conclusion

So, there you have it – hospitals are getting a serious tech upgrade with AI, making healthcare smarter, faster, and more patient-centric. 

For more related content, take a look at 10 Case Studies: Hospitals Embracing AI and Highest Paying Healthcare tech Jobs (2023 Data).