Streamlined Medical Practice with Ambient Clinical Intelligence

Streamlined Medical Practice with Ambient Clinical Intelligence

by Ree AI Health Tech Med Tech

Since the onset of the pandemic, more healthcare workers and clinicians have experienced burnout, leading to dissatisfaction among both patients and clinicians. Overworked clinicians often make errors in their documentation, and their lack of time and stressed demeanor can erode the trust between physicians and patients. Dissatisfied and neglected patients are less likely to engage with their care, adhere to care plans, and follow preventive healthcare advice, increasing the likelihood of adverse outcomes (DeepScribe, 2023).

In Medscape’s 2021 physician survey, 42% of physicians reported feeling burned out, citing “too many bureaucratic tasks” and “spending too many hours at work” as the main causes. Providers often spend hours documenting patient care, and the administrative burden often stretches into their own time. The Association of American Medical Colleges projects a shortfall of nearly 122,000 physicians in the US by 2032 (Harper, 2022).

Ambient Clinical Intelligence (ACI) is a technology that can help alleviate the burden of medical documentation for clinicians, among many other benefits we’ll explore in this article. But first, let’s get a better understanding of ACI.

Contents

What is Ambient Clinical Intelligence?

Robot sitting in a patient room

ACI brings together several technologies that work together to improve healthcare:

  • Ambient intelligence
  • Artificial intelligence (AI)
  • Data analytics
  • Internet of Things (IoT)
  • Natural Language Processing (NLP)

ACI in healthcare includes IoT-based tools such as temperature and humidity sensors, blood pressure monitors, and other devices that autonomously collect data and continuously update doctors on the vital statistics of critical patients (Joshi, 2022).

“Imagine a hospital where every room, every corridor, every piece of equipment is interconnected, constantly gathering data, analyzing it, and providing insights,” says Jon Morgan, CEO and Editor-in-Chief of VentureSmarter. “It means doctors and nurses have access to a wealth of information right at their fingertips, allowing for quicker and more accurate diagnoses. This can significantly improve patient outcomes because decisions are based on a comprehensive analysis of real-time data rather than just a snapshot in time.” 

Let’s explore how ACI can make healthcare tasks more efficient in both healthcare settings and patients’ homes. 

infographic with statistics on different ACI use cases and RPM

ACI Use Cases for Clinical Spaces

ACI can improve the quality of health services by making many processes more efficient, such as:

  • Transcribing medical notes
  • Creating reports
  • Patient monitoring

This section describes some of ACI’s biggest benefits in healthcare settings.

Clinical documentation during patient care

Doctors looking at paperwork together

ACI technology can help alleviate the burden of medical documentation for clinicians, allowing them to give their full attention to patients during visits while ACI creates accurate clinical notes directly in the electronic health record (EHR) for review (Augnito, 2023). (This a concept included in the fancier term, “AI-powered medical documentation automation.”) ACI can also spot indicators of depression, anxiety, and social determinants of health (SDoH) during patient-physician conversations (Harper, 2022).

In one study, a deep learning (DL) model trained on 14,000 hours of outpatient audio from 90,000 conversations between patients and physicians. The transcription accuracy of the DL version was 80%, compared to 76% accuracy by medical scribes (Haque et al., 2020). 

In another example, a medical provider found that microphones attached to eyeglasses reduced documentation time from 2 hours to just 15 minutes. This huge time savings doubled the time spent with patients (Haque et al., 2020).

By automating routine tasks and documentation, ACI allows healthcare providers to spend more time focusing on direct patient care, leading to patient satisfaction.

Patient satisfaction

A nurse speaking to patient

The automation of ACI can help strengthen the patient-physician relationship and increase patient satisfaction, engagement, and retention. 

“Using systems that can automatically monitor patients’ vital signs, track medication administration, and even predict potential complications,” Morgan says.  “Healthcare professionals can focus more on direct patient care rather than spending time on administrative tasks. This improves the overall quality of care while also reducing the burden on healthcare workers in today’s overstretched healthcare systems.”

Tests and reports

With ACI tools, hospitals can conduct tests on patients and monitor them autonomously with wireless sensors and wearable devices. 

For example, an ambient intelligence sensor monitors a patient’s health by dynamically tracking their vitals. First, it collects and assesses vitals, body fat, blood sugar, cholesterol levels, and other details. Then it can create a report listing potential illnesses and recommendations on diagnoses, medical coding, diet, medications, and lifestyle (Joshi, 2022).

By enhancing data interoperability, ACI eliminates the need for redundant paperwork and testing. ACI can streamline care coordination by compiling data from various sources into consolidated dashboards, providing clinicians with a holistic view of each patient. Reviewing these dashboards can help them better understand their patients’ clinical history, medications, test results, and more (Augnito, 2023). 

Tracking infectious disease

IoT, thermal vision cameras, and AI can check infected zones, such as surfaces where infectious viruses are found, and ensure they are cleaned and decontaminated. Thermal vision cameras are also useful for monitoring crowded areas and tracking individuals who may carry a contagious disease (Joshi, 2022).

Surgical training

In the operating room (OR), ambient cameras can be used for endoscopic videos to improve surgical training. Ambient intelligence can also account for surgical objects in the OR, including those that could be left inside a patient during a procedure, to mitigate staff errors (Haque et al., 2020).

Continuous patient monitoring in the ICU

In one study, ambient sensors in hospital intensive care units (ICUs) monitored the movements of patients, clinicians, and visitors with over 85% accuracy.

In another study, sensors installed above hand sanitizer dispensers across a hospital unit were 75% accurate in measuring handwashing compliance within one hour, while a human observer was only 63% accurate (Haque et al., 2020).

Patient in ICU with monitor in foreground

Observing patients post-surgery

Ambient intelligence in recovery rooms post-op can continuously observe recovery-related behaviors, giving providers insight into movement and other activities. This can reduce recovery time and improve post-surgical outcomes (Joshi, 2022).

While ACI offers numerous benefits in clinical spaces, its potential extends beyond hospital walls.

ACI for Aging in Place: Enhancing Independent Living

By 2050, the world’s population aged 65 years or older will increase from 700 million to 1.5 billion (Haque et al., 2020). As people live longer, their independent living, chronic disease management, physical rehabilitation, and mental health become paramount. 

Promoting autonomy for patients with remote patient monitoring (RPM)

Activities of daily living (ADLs), such as bathing, dressing, and eating, are critical to the well-being and independence of aging adults. Aging and elderly patients living at home are at an increased risk for falls, accidents, and emergencies. Impairment in performing ADLs is associated with a twofold increased risk of falling, and up to a fivefold increase in the one-year mortality rate (Haque et al., 2020).

RPM through ACI can analyze their daily activities to detect significant changes that may need a closer look. It can also help identify changes in vital signs, movement patterns, sleep rhythms, behaviors, and emerging symptoms that may signal a decline in a patient’s quality of life. Ambient-assisted living using the ACI-RPM combo can also monitor patients for early signs of dementia and Alzheimer’s. 

“The constant monitoring and analysis of patient data in real-time can help in early detection of health issues,” says Collen Clark, Medical Malpractice Lawyer and Founder of Schmidt & Clark LLP. “This allows for quicker interventions and personalized treatment plans, while reducing the risk of medical errors, which can have legal implications related to negligence or malpractice.” 

Any concerning findings from RPM automatically trigger alerts to healthcare providers, allowing them to intervene early with quick, proactive outreach to patients in need. This can prevent avoidable ER visits, hospitalizations, and health emergencies (Augnito, 2023).

Wearable sensors for monitoring and fall detection in seniors

Two doctors chatting in a hallway

Wearable devices such as accelerometers or electrocardiogram sensors can track not only ADLs but also heart rate, glucose level, and respiration rate. They can even remind patients to take their medications (Haque et al., 2020), and detect falls.

As wearable devices and IoT ecosystems in healthcare continue to expand, integrating them with ACI systems can provide continuous personalized monitoring and truly ambient intelligent care. 

Patients can get proactive alerts about potential health issues before they become critical, and get customized recommendations. Streamlining the flow of data from personal sources like fitness trackers to electronic health records via ACI can massively enrich patient profiles for highly tailored care (Augnito, 2023).

Ambient sensors

In one study, researchers installed a depth and thermal sensor inside the bedroom of an older individual and observed 1,690 activities during one month, including 231 instances of caregiver assistance. A convolutional neural network was 86% accurate at detecting assistance. In a different study, researchers collected ten days of video from six individuals in an elderly home and achieved similar results (Haque et al., 2020).

Although the data from visual sensors are promising, they raise privacy concerns in some places like bathrooms, where grooming, bathing and toileting activities occur. To counter this, researchers also explored acoustic and radar sensors. One study used microphones to detect showering and toileting activities with accuracy rates of 93% and 91%, respectively (Haque et al., 2020). 

ACI has tremendous potential. However, it’s important to consider some challenges and limitations.

ACI Caveats and Considerations 

Flatlay of small medical items

The use cases and benefits of ACI are remarkable, but as with any technology, there are still considerations to gain its maximum benefit in the larger healthcare ecosystem.

Bias

ACI systems are dependent on the quality of data used to train algorithms. If that data reflects societal biases, the AI could make flawed judgments and recommendations. There’s also the risk of over-reliance on AI diagnostics versus human expertise. Careful oversight is required to audit algorithms and ensure AI transparency in clinical decision-making (Augnito, 2023).

Data privacy and security

There is a heightened risk of unauthorized access or breaches with ACI. Patients have a right to understand how their data is used with ACI tools during consultations and treatment. Health providers should disclose this information and request patient consent, which is optional. 

“With the continuous stream of patient data being collected, stored, and analyzed by ACI systems, there’s a heightened risk of unauthorized access or breaches,” Clark says. “I would advise hospitals to invest in robust data protection measures and ensure compliance with relevant regulations such as HIPAA. It’s essential to strike a balance between leveraging the benefits of ACI and safeguarding patient privacy to avoid legal repercussions.”

Computational methods to protect privacy include (Haque et al., 2020):

  • differential privacy (adds noise to the collected data)
  • face blurring
  • dimensionality reduction (pixelated images)
  • body masking (replaces people’s images with faceless avatars)
  • federated learning (gradient updates)
  • homomorphic encryption

There is a trade-off between the level of privacy protection provided by each method and the required computational resources. 

Strict regulations around data encryption, access controls, and auditing will be necessary to prevent breaches and protect patient rights. 

Medical decision making

Clark makes a final warning about implementing ACI systems to automate note-taking and other tasks in hospitals. She says shifting responsibility from the clinician to ACI “… could lead to legal discussions around liability in cases where decisions are influenced by AI. It’s crucial for hospitals and medical professionals to establish clear protocols and guidelines, and for legal frameworks to adapt to these changing dynamics, ensuring accountability without stifling technological advancements.”

By seamlessly integrating ACI into healthcare workflows, providers can streamline operations, enable continuous monitoring of patients, and leverage data-driven insights to inform diagnostic and treatment decisions. This integration can significantly improve patient outcomes and reduce the burden on healthcare workers, and ultimately enhance the quality of care they provide.

References

Augnito. How Ambient Clinical Intelligence is Advancing Real-Time Patient Care.

DeepScribe. Ambient Clinical Intelligence—What is it and how will it transform healthcare?

Harper, K. What is ambient clinical intelligence—and how is it transforming healthcare? Nuance. June 16, 2022.

Haque A., Milstein A., & Fei-Fei L. Illuminating the dark spaces of healthcare with ambient intelligence. Nature. 2020; 585(7824):194-198. doi:10.1038/s41586-020-2669-y 

Joshi, N. The Myriad of Applications of Ambient Intelligence in Healthcare. Forbes. January 9, 2022.

How Machine Learning and Deep Learning are Advancing Modern Healthcare

How Machine Learning and Deep Learning are Advancing Modern Healthcare

by Ree AI Health Tech Med Tech

The healthcare industry is undergoing profound changes, driven by the rapid advancements in artificial intelligence (AI). Machine learning (ML) and deep learning (DL) are reshaping how we approach patient care, diagnose illnesses, treatment, and drug discovery. According to a recent study by Accenture, the AI health market is expected to reach $6.6 billion by 2021, growing at a compound annual growth rate of 40%. 

This article explores the impact of ML and DL in healthcare, including their key applications, challenges, and the potential to improve patient outcomes and healthcare accessibility, and shape the future of medical research.

Contents

Understanding Machine Learning and Deep Learning in Healthcare

Flatlay of several small medical devices

ML and DL are two closely-related, yet distinct subfields of AI that have several uses in healthcare. To fully appreciate their impact, it’s crucial to understand their definitions, differences, and benefits in medical contexts.

ML in healthcare

ML develops algorithms and statistical models to help computers improve their performance on specific tasks (Rajkomar, Dean, & Kohane, 2019). In healthcare, ML algorithms can analyze huge amounts of medical data to identify patterns, make predictions, and generate insights that can aid in clinical decision-making.

Key characteristics of ML in healthcare include:

  • Ability to process large volumes of data
  • Continuous improvement through exposure to new data
  • Potential to automate routine tasks and improve efficiency

DL: a powerful subset of ML

DL is a type of ML that uses artificial neural networks with many layers to help computers understand and process complex patterns in data (LeCun, Bengio, & Hinton, 2015). These neural networks are inspired by the structure and function of the human brain, allowing them to learn hierarchical representations of data.

In healthcare, DL has shown remarkable success in:

  • Interpreting medical images (e.g., X-rays, MRIs, CT scans)
  • Analyzing genomic data for precision medicine (personalized medicine)
  • Natural language processing (NLP) of clinical notes and medical literature

Key differences between traditional analytics and ML/DL approaches

Traditional analytics and ML/DL approaches differ in several important ways, as shown in the following table.

ApplicationTraditional AnalyticsML/DL
Data handlingRelies on structured data and predefined rulesCan process both data and learning patterns autonomously
ScalabilityLimited by the human capacity to interpret resultsCan scale to analyze massive datasets and complex relationships
AdaptabilityRequires manual updates to models and rulesContinuously learns and adapts to new data
Feature extractionRequires manual feature engineering
Automatically learns relevant features from raw data
Comparison of traditional analytics and ML/DL in 4 applications

Benefits of using ML and DL in healthcare settings

Nurse's hands touching screen of medical equipment

The integration of ML and DL in healthcare has many benefits:

1. More accurate diagnostics: ML and DL algorithms can analyze medical images and patient data with high precision, often matching or exceeding human expert performance (Topol, 2019).

2. Early disease detection: By identifying subtle patterns in patient data, these technologies can flag potential health issues before they become severe.

3. Personalized treatment plans: ML algorithms can examine the unique traits of each patient and recommend tailored treatment strategies.

4. Efficient resource allocation: Predictive models can help healthcare providers optimize staffing, bed management, and equipment utilization.

5. Faster drug discovery: ML and DL can significantly speed up identifying potential drug candidates and predicting their effectiveness.

6. Better patient engagement: AI-powered chatbots and virtual assistants can provide 24/7 support and information to patients.

7. Lower healthcare costs: By improving efficiency and accuracy, ML and DL can help reduce unnecessary procedures and hospitalizations.

DL Breakthroughs in Medical Diagnostics

DL has made significant strides in medical diagnostics, offering new levels of accuracy and efficiency. This section covers some of the most notable breakthroughs that are pushing the boundaries of medical diagnostics.

Advanced image recognition in radiology and pathology

DL algorithms have demonstrated remarkable capabilities in analyzing medical images:

  • Radiology: Convolutional Neural Networks (CNNs) can detect and classify abnormalities in X-rays, CT scans, and MRIs with high accuracy. For example, a Stanford University model showed dermatologist-level performance in classifying skin lesions, including malignant melanomas (Miotto et al., 2017).
  • Pathology: DL models can analyze digital pathology slides to detect cancer cells and other abnormalities. A study by Nature Medicine showed that a DL algorithm can detect prostate cancer in biopsy samples with an accuracy comparable to that of expert pathologists (Campanella et al., 2019).

NLP for clinical documentation

Nurse standing in a recovery room

NLP, powered by DL, is changing the way health providers process clinical notes and medical literature (IMO Health, 2024):

  • Pulling relevant information from clinical notes automatically
  • Improving medical coding for billing and research purposes
  • Analyzing clinical conversations in real-time for documentation and decision support

For example, researchers at MIT and Beth Israel Deaconess Medical Center developed an NLP system that can analyze doctor-patient conversations to identify medically relevant information and help with clinical documentation (Finlayson et al., 2018).

Early detection of diseases through pattern recognition

DL models can identify subtle patterns in patient data that may indicate the early stages of diseases:

  • Detecting early signs of Alzheimer’s disease from brain scans and cognitive test results
  • Recognizing precancerous lesions in colonoscopy images
  • Predicting the onset of sepsis in intensive care unit (ICU) patients (Nemati et al., 2018)

A notable example is a DL algorithm developed by Google Health and DeepMind, that can detect signs of breast cancer in mammograms up to two years before it becomes clinically evident (McKinney, S.M. et al., 2020).

Wearable device data analysis for continuous patient monitoring

DL allows more advanced data analysis from wearable devices such as (Price, 2024):

  • Detecting atrial fibrillation and other cardiac arrhythmias from smartwatch data
  • Predicting flare-ups of chronic conditions like asthma or COPD
  • Tracking physical activity and sleep patterns to assess one’s general health 

For example, Cardiogram and the University of California, San Francisco developed a DL model that showed 97% accuracy in detecting atrial fibrillation using heart rate data from Apple Watches (Topol, 2019).

ML applications transforming healthcare practices

Nurse standing in a radiology room

The healthcare sector is using ML across the spectrum, transforming various aspects of patient care, medical research, and healthcare management. 

Predictive Analytics for Patient Risk Assessment

One of the most promising uses of ML in healthcare is its ability to predict patient risks and outcomes. ML can analyze large datasets of patient information, including electronic health records (EHRs), genetic data, and lifestyle, which can help healthcare providers do things like:

  • Identify patients at high risk of getting specific diseases
  • Predict the likelihood of a patient returning to the hospital 
  • Predict potential complications during medical procedures

For example, a study published by Nature Medicine showed a DL model can predict acute kidney injury up to 48 hours before its onset, allowing for early intervention and potentially saving lives (Tomašev, et al., 2019).

Drug discovery and development

ML is transforming the pharmaceutical industry by speeding up the drug discovery process and reducing costs. Key applications include:

  • Virtual screening of chemical compounds to identify potential drug candidates
  • Predicting drug-target interactions and side effects
  • Optimizing clinical trial design and patient selection

A notable success story is with Atomwise, who used ML to identify potential treatments for the Ebola virus, significantly reducing the time and resources required for initial drug screening (Ekins, S. et al., 2019).

Medical imaging analysis and interpretation

Illustration of patient with brain scans onscreen

ML and DL algorithms have shown remarkable accuracy when analyzing medical images, often matching or surpassing human experts. Use cases include:

  • Detecting and classifying tumors in radiology images
  • Identifying diabetic retinopathy in eye scans
  • Analyzing pathology slides for cancer diagnosis

For example, a DL algorithm developed by Google Health showed the ability to detect breast cancer in mammograms with greater accuracy than human radiologists, potentially reducing false negatives by 9.4% (McKinney, S.M. et al., 2020).

EHR management and analysis

ML is helping healthcare providers make better use of the vast amounts of data stored in EHRs by:

  • Automating medical coding and billing processes
  • Identifying patterns in patient data to improve care quality
  • Enhancing clinical decision support systems

A study published by JAMA Network Open showed that an ML model can predict the risk of sepsis in hospitalized patients up to 12 hours before clinical recognition, using only data from the EHR (Nemati, S. et al., 2018).

Personalized treatment plans and precision medicine

ML algorithms can analyze a patient’s unique traits, including genetic makeup, lifestyle factors, and treatment history, to recommend personalized treatment strategies by:

  • Predicting patient response to specific medications
  • Optimizing dosage and treatment schedules
  • Identifying potential adverse drug reactions

For example, IBM Watson for Oncology uses ML to analyze patients’ medical records and scientific literature to recommend evidence-based treatment plans for cancer patients (Somashekhar, S.P. et al., 2018).

Improving Patient Care with AI-powered Solutions

AI can not only revolutionize diagnostics and treatment, but also enhance patient care and engagement at the bedside. 

Robot reviewing scans on screen

Virtual health assistants and chatbots for patient engagement

AI virtual assistants and chatbots are transforming patient communication and support with (Healthcare Communications, 2024):

  • 24/7 availability to answer patient queries and provide health information
  • Triage of patient symptoms and guidance on appropriate care pathways
  • Medication reminders and support for medical adherence 

For example, Babylon Health’s AI chatbot can assess patient symptoms, provide health information, and even book appointments with healthcare providers when necessary.

Remote Patient Monitoring (RPM) and telehealth advancements

AI enhances RPM and telehealth capabilities in various ways such as (Health Resources and Services Administration, 2024):

  • Continuous analysis of patient-generated health data from wearables and home monitoring devices
  • Predictive analytics to identify patients at risk of deterioration
  • AI-assisted video consultations for more accurate remote diagnoses

A study published by npj Digital Medicine showed that an AI-powered remote monitoring system can reduce hospital readmissions for heart failure patients by 38% (Mittermaier et al., 2023).

Automated appointment scheduling and resource allocation

AI algorithms can optimize healthcare operations in various ways with:

  • Intelligent scheduling systems that consider patient preferences, urgency, and provider availability (Coursera, 2024) 
  • Predictive models for patient no-shows and overbooking strategies
  • Best use of hospital resources based on the anticipated patient inflow

For example, Boston Children’s Hospital implemented an AI-powered scheduling system that reduced wait times for MRI appointments by 25%, while increasing daily scan volume (NanoHealthSuite, 2024).

Personalized health recommendations based on individual data

AI makes it possible to provide highly personalized health recommendations:

  • Tailored lifestyle and dietary suggestions based on a patient’s genetic, health, and behavioral data
  • Personalized exercise plans based on individual progress and preferences
  • AI-driven health coaching to manage chronic illnesses

An example is the AI-powered health coach developed by Lark Health, which provides personalized guidance for diabetes prevention and management, and shows significant improvements in patient outcomes (Bounteous, 2024).

Navigating AI in Healthcare: Challenges and Ethical Considerations

While the potential benefits of ML and DL in healthcare are undeniable, their use also presents several challenges and ethical considerations to address.

Illustration of two levels in a hospital

Data privacy and security concerns

There are serious privacy concerns when using large-scale patient data for ML and DL, as noted by Esteva et al. (2019):

  • The risk of data breaches and unauthorized access to sensitive health information 
  • Challenges to maintain patient anonymity in large datasets
  • Finding a balance between data sharing for research and individual privacy rights

To address these issues, health providers must use robust data security strategies such as differential privacy techniques and secure multi-party computation.

Bias in AI algorithms and dataset representation

AI systems can perpetuate or amplify existing biases in healthcare:

  • Certain demographic groups are underrepresented in training data (Topol, 2019)
  • Algorithmic bias can lead to disparities in diagnosis or treatment recommendations
  • Potential to reinforce existing healthcare inequalities

Researchers and developers are working on methods to detect and mitigate bias in AI algorithms, such as the use of fairness-aware machine learning techniques (Vial, 2024).

Integration of AI systems with existing healthcare infrastructure

The use of AI solutions in healthcare settings presents technical and organizational challenges such as:

  • Interoperability issues between AI systems and legacy healthcare IT systems (Coursera, 2024)
  • Resistance to change among healthcare professionals
  • Need for extensive training and support for AI system users

Successful integration requires a collaborative approach involving healthcare providers, IT professionals, and AI developers to ensure seamless adoption and application of AI technologies (Flam, 2024).

Regulatory compliance and FDA approval processes

As with many other forms of technology, the rapid advancement of AI in healthcare has outpaced our current regulatory frameworks, including:

  • Uncertainty about the classification and approval process for AI-based medical devices
  • Challenges when validating continuously learning AI systems
  • Balancing innovation with patient safety concerns

The FDA has been working on developing new regulatory approaches for AI/ML-based software as a medical device (SaMD), including a proposed regulatory framework for modifications to AI/ML-based SaMD (Everson et al., 2024).

Charting the Course: A Roadmap for the Future of ML and DL in Healthcare

As ML and DL continue to evolve, their impact on healthcare is expected to grow exponentially. This section shares some key trends and potential developments.

Person holding a vial near a microscope in a lab

Federated learning: Allowing multiple institutions to train collaborative models together, without sharing raw patient data.

Explainable AI: Developing interpretable ML models to increase trust and adoption among healthcare professionals.

Edge computing: Bringing AI capabilities closer to the point of care for real-time analysis and guidance.

Potential for AI to address global health disparities

AI has the potential to improve healthcare access and quality in underserved regions:

  • AI-powered diagnostic tools for resource-limited settings
  • Telehealth solutions to connect remote areas with specialist care
  • Predictive models for disease outbreaks and public health planning

For example, a DL model developed by researchers at Stanford University showed promise in diagnosing pneumonia from chest X-rays in areas lacking expert radiologists (Price, 2024).

Collaboration between healthcare professionals and AI researchers

The future of healthcare AI will likely involve closer collaboration between clinicians and AI experts (Topol, 2019):

  • Interdisciplinary research teams to create AI solutions for clinical settings
  • Integration of AI education into medical curricula
  • Continuous feedback loops between AI developers and healthcare providers

Systems of continuous learning for flexible healthcare delivery

The development of AI systems that can learn and adapt in real-time to revolutionize healthcare delivery:

  • AI models that update based on new clinical data and patient outcomes
  • Personalized treatment plans that change with patient responses
  • Adaptive clinical decision support systems that improve over time

AI in Healthcare: Transforming Medicine and Shaping Our Future 

The integration of ML and DL in healthcare represents a paradigm shift in how we approach patient care, medical research, and health system management. While challenges remain, the potential benefits of these technologies in improving health outcomes, reducing costs, and enhancing the overall quality of care are limitless. 

As these technologies continue to evolve, healthcare providers, researchers, and policymakers must work together to address challenges and ensure responsible implementation. To fully realize the transformative potential of AI in medicine, it’s imperative to address ethical concerns, ensure equal access to AI-powered healthcare solutions, and foster collaboration between technology experts and healthcare professionals.

This article has explored the various applications of machine learning and DL in healthcare, from diagnostic tools to personalized treatment plans. We’ve discussed the challenges and ethical considerations that come with implementing these technologies, as well as the exciting possibilities for the future of healthcare. As AI continues to evolve, it will undoubtedly play an increasingly important role in shaping the future of medicine and improving patient outcomes worldwide.

References

Alkhaldi, Nadejda. (2024). Predictive analytics in healthcare: 7 ways to save time and money. ITRex Group.

Bounteous. (2024). AI Transforms Personalized Care for Better Health Outcomes.

Campanella, G. et al. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Natural Medicine. 25, 1301-1309.

Coursera. What Is Machine Learning in Health Care?

Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., … & Freundlich, J. S. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463-477.

Everson, J., Smith, J., Marchesini, K., & Tripathi, M. (2024). A Regulation to Promote Responsible AI in Health Care. Health Affairs.

Finlayson, S.G. et al. (2018) Conversational AI: The Science Behind the Alexa Prize. arXiv:1801.03604 

Flam, S. ForeSee Medical. Machine Learning in Healthcare.

Habehh, H., and Gohel, S. (2021). Machine Learning in Healthcare. Current Genomics. 16;22(4):291-300. doi:10.2174/1389202922666210705124359

Health Resources and Services Administration. Telehealth and Remote Patient Monitoring.

Healthcare Communications. Virtual Assistants and Chatbots in Healthcare.

IMO Health. Natural Language Processing 101: A Guide to NLP in Clinical Documentation.

LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature. 521, 436-444.

Li, M., Jiang, Y., Zhang, Y., & Zhu, H. (2023). Medical image analysis using deep learning algorithms. Frontiers in Public Health, 11, 1273253. doi:10.3389/fpub.2023.1273253

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., … & Suleiman, A. (2020). International evaluation of an AI system for breast cancer screening. Nature. 577(7788), 89-94.

Mittermaier, M., Raza M.M., & Kvedar, J.C. Bias in AI-based models for medical applications: challenges and mitigation strategies.npj Digital Medicine. 6:113. doi:10.1038/s41746-023-00858-z

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics vol. 19,6 (2018): 1236-1246. doi:10.1093/bib/bbx044

NanoHealthSuite. Predictive Analytics and Risk Assessment in Healthcare.

Nemati, S. et al. (2018). An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine. 46, 547-553.

Price, Claude. (2024). Harnessing wearable technology for real-world data.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

Somashekhar, S.P. et al. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology. 29, 418-423.

Tomašev, N. et al. (2019). A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 572, 116-119.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Vial. The Role of Machine Learning in Drug Design: Advancements and Challenges.

AI in Pharmaceutical Research: How Machine Learning Accelerates Drug Discovery and Development

AI in Pharmaceutical Research: How Machine Learning Accelerates Drug Discovery and Development

by Ree AI Health Tech Med Tech

AI in pharmaceutical research is booming. Artificial intelligence (AI) and machine learning (ML) analyze enormous volumes of clinical and biological data with amazing speed and accuracy, allowing them to generate and evaluate a wide range of medication formulation options. Let’s learn more about how they do it.

Contents

ML and AI-driven applications in pharma: from research to discovery

Generative AI can help address complex formulation challenges and develop personalized medicines (UsefulBI, 2024). Combined with ML, AI also brings new opportunities for disease diagnosis, medical imaging, treatment personalization, drug safety monitoring, drug repurposing, and big data analysis to make better decisions (Vamathevan, J., et al., 2019).

ML techniques like supervised learning, and reinforcement learning, and their applications can help facilitate pharmaceutical operations (Wadighare and Deshmukh, 2024). These applications include:

  • Drug discovery and design
  • Research and development (R&D)
  • Disease prevention and diagnosis
  • Epidemic prediction
  • Email detection
  • Speech recognition
  • Data mining

Large-scale data analysis is the foundation of these applications. Next, we’ll explore how big data analytics is transforming drug development.

Big data analytics in drug development turn information into insights

The explosion of biological and clinical data such as genomics, imaging, and the use of digital wearable devices has created both opportunities and challenges for drug developers. ML techniques are invaluable to glean meaningful insights from this deluge of information, informing decision-making at every stage of the drug development process (Topol, 2019).

Recursion is a company leveraging big data analytics in a way never seen before. Conducting over 2 million experiments per week, they generate and store 20 to 25 petabytes of data on their in-house supercomputer, Biohive-1. They’ve also partnered with NVIDIA to use its DGX Cloud supercomputing power, allowing them to predict the targets of 36 billion molecules (Brazil, 2024).

Such methods also offer benefits after market research with the use of “big data” from real-world data sources. These sources can enrich the understanding of a drug’s benefit-risk profile, better understand treatment sequence patterns, and identify subgroups of patients who may benefit more from one treatment compared with others, or precision medicine (Schneider, 2018).

Close up of shelves with medication

Smarter medicines: How AI can optimize drug formulations

AI can create more stable and effective medications with improved drug delivery systems. According to UsefulBI, Yang, and Topol, AI can also:

  • Predict drug properties.
  • Optimize dosage forms. 
  • Detect potential drug interactions, providing warnings to healthcare professionals to prevent harmful combinations of medications.
  • Suggest novel excipients, particularly useful in addressing complex formulation challenges and developing personalized medicines.

These capabilities are especially valuable in developing new formulations that optimize for specific characteristics such as stability, bioavailability, or controlled release profiles (UsefulBI, 2024). 

The integration of generative models in de novo drug design is of particular interest. These models can create entirely new molecular structures that are optimized for specific properties, potentially leading to the discovery of novel chemical entities with superior drug-like characteristics. 

Epidemic prediction

One significant application is in epidemic prediction. Pharmaceutical companies and healthcare industries are using ML and AI technologies to monitor and verify the spread of infections worldwide. These modern technologies consume data from various sources, analyzing environmental, biological, and geographical factors affecting population health in different geographical areas. This approach helps predict and even mitigate the impact of future epidemics (Bullock et al., 2020). 

Man and woman working in a lab with flasks

Pharmacovigilance (drug safety)

In the field of pharmacovigilance, AI and ML algorithms can help pharmaceutical companies and regulatory agencies identify potential safety issues with medications more quickly. This capability is crucial for ensuring patient safety and refining drug formulations (Bate et al., 2018). 

Moreover, AI is being used to optimize drug formulations, creating more stable and effective medications with improved drug delivery systems. It can also detect potential drug interactions, providing warnings to healthcare professionals to prevent harmful combinations of medications (Yang et al., 2019).

Supply chain and manufacturing optimization

Beyond research and development, ML is also making significant contributions to supply chain and manufacturing optimization in the pharmaceutical industry. It’s being used to predict demand, optimize inventory levels, and improve quality control in manufacturing processes. In drug marketing and sales, ML algorithms can analyze market trends, predict drug performance, and optimize marketing strategies (Ramanathan, 2023). 

One of the most crucial applications of AI in drug discovery is target identification.

Target identification powered by AI and ML 

Illustration of 3 people in a lab

One of the most crucial and time-consuming steps in drug discovery is identifying viable therapeutic targets. Traditionally, this process could take years of painstaking research. However, AI-powered target identification is dramatically accelerating this phase, allowing researchers to sift through enormous amounts of biological data with unprecedented speed and accuracy (Schneider, 2018). 

AI is widely used for multi-target drug innovation and biomarker identification, offering efficiency and accuracy that were previously unattainable. Pharmaceutical companies are using AI-powered tools and ML algorithms to streamline drug research, development, and innovation processes around the world (Wadighare and Deshmukh, 2024).

ML algorithms can analyze complex datasets like genomic, proteomic, and clinical data, to identify and study disease patterns, and determine which composite formulations are best suited for treating specific symptoms of particular diseases. These AI systems can detect patterns and relationships that might be overlooked by human researchers, to discover novel targets and pathways (Ching et al., 2018). 

ML is also being used to predict protein structures, design new molecules, and simulate drug-target interactions, significantly speeding up the drug discovery process (Ramanathan, 2023). These approaches not only accelerate the drug discovery process, but also have the potential to address rare diseases more effectively. 

Examples

Companies like Benevolent AI are at the forefront of this revolution. Their platform connects structured data from clinical and chemical databases with unstructured data from scientific literature, creating what they call “an enormous hairball of interconnected facts.” This approach allowed them to identify PDE10 as a novel target for ulcerative colitis, a connection not explicitly stated in existing literature (Brazil, 2024).

Another notable success story in AI-driven target identification comes from Insilico Medicine, whose AI platform helps them predict the best formulations, reducing the need for trial-and-error experimentation and accelerating the development process (UsefulBI, 2024). Insilico’s AI-generated anti-fibrotic drug became the first of its kind to reach Phase 2 clinical trials. This milestone demonstrates the potential of AI to not only identify targets but also to guide the entire drug discovery process from conception to clinical testing (Insilico Medicine, 2024).

While identifying targets is crucial, predicting the properties of potential drug candidates is equally important. That’s where deep learning comes into play.

Deep learning for molecular property prediction

AI image of a colorful molecular compound

Deep learning has revolutionized the field of molecular property prediction, enabling researchers to assess the potential of drug candidates with remarkable accuracy. This technology is particularly valuable in predicting Absorption, Distribution, Metabolism, and Excretion (ADME) properties and toxicity, crucial factors in determining a drug’s viability (Yang et al., 2019).

Compared to traditional Quantitative Structure-Activity Relationship (QSAR) methods, modern deep learning approaches offer several advantages. They can handle larger and more diverse datasets, capture non-linear relationships more effectively, and often require less manual feature engineering (Gao, et al., 2020). For instance, graph neural networks have shown exceptional performance in predicting molecular properties by directly learning from the structural representation of molecules (Wu et al., 2018).

Real-world applications of deep learning in property prediction are already yielding impressive results. Pharmaceutical companies are using these models to screen huge libraries of compounds, significantly reducing the time and cost associated with early-stage drug discovery (Zhavoronkov et al. 2019). For example, deep learning models have been successfully employed to predict drug-induced liver injury, a major cause of drug attrition in clinical trials (Xu et al., 2015)

However, it’s important to note that while deep learning models excel at pattern recognition, they may struggle with extrapolation to novel chemical spaces. Researchers are addressing this limitation by developing more robust models and incorporating techniques like transfer learning and multi-task learning to improve generalization (Goh et al., 2017).

Predictive modeling

Man and woman working in a lab wearing masks

In the pre-clinical space, natural language processing (NLP) is being used to extract scientific insights from biomedical literature, unstructured electronic medical records (EMR), and insurance claims to ultimately help identify novel targets. 

Predictive modeling is another area where ML is making significant strides in clinical trial design. Predictive modeling can predict protein structures and facilitate molecular compound design and optimization, enabling the selection of drug candidates with a higher probability of success (Ching et al., 2018). In addition, ML plays a crucial role in genomics and proteomics research, helping to identify genetic markers associated with diseases and potential drug targets (Ramanathan, 2023). 

By analyzing historical trial data and incorporating real-world evidence, these models can forecast potential outcomes and identify potential pitfalls before a trial begins. This foresight allows researchers to optimize trial protocols and resource allocation, potentially saving millions of dollars and years of development time (Gayvert, 2016).

Despite these promising applications, the use of AI in clinical trials raises important ethical considerations and regulatory challenges. Ensuring patient privacy, addressing potential biases in AI algorithms, and maintaining transparency in decision-making processes are crucial concerns that the industry must navigate. Regulatory bodies like the FDA are working on developing guidelines for the use of AI in drug discovery and clinical trials to address these issues (FDA, 2023).

With promising drug candidates identified, the next challenge lies in designing effective clinical trials to test these compounds.

Clinical trial design optimization

Group of researchers in a clinical trial

In the realm of clinical data assessments, AI and ML are revolutionizing how healthcare data is analyzed and utilized. These technologies are being applied in various areas, including disease diagnosis, medical imaging analysis, treatment personalization, and clinical trial optimization (Alam et al., 2023). 

The application of ML in clinical trial design is transforming how pharmaceutical companies approach this critical phase of drug development.

ML applications in clinical trial design

ML is transforming clinical trial optimization to improve patient recruitment, predict patient dropout rates, and optimize trial design. AI-driven patient selection and stratification are enabling more targeted and effective trials, potentially reducing the high failure rates that have long plagued the pharmaceutical industry.

Advanced techniques like Bayesian nonparametric learning are emerging as powerful tools in clinical trial design and analysis. These methods allow flexible shrinkage modeling for heterogeneity between individual subgroups and automatically capture additional clustering, requiring fewer assumptions than more traditional methods (Kolluri et al., 2022). 

ML algorithms can analyze patient data such as genetic information, medical history, and lifestyle factors, to identify the most suitable candidates for a trial. This precision approach not only increases the likelihood of trial success but also helps in developing more personalized treatments (Woo, 2019).

AI applications in clinical trial design

AI-driven patient selection and stratification enable more targeted and effective trials, potentially reducing the high failure rates that have long plagued the industry (Harrer et al., 2019).

Big pharmaceutical companies are leveraging AI for clinical trial design as well. For example, GSK developed its own in-house large language model (LLM) called Jules OS, capable of autonomously performing tasks and responding directly to staff questions. The company has used AI “right across the value chain” since 2019, including in clinical trial design for drugs like bepirovirsen, their investigational treatment for chronic hepatitis B (Bender & Cortés-Ciriano, 2021).

However, it’s crucial to strike a balance between computational predictions and experimental validation. While AI can significantly narrow down the search space and suggest promising drug candidates, the complexity of biological systems means that experimental testing remains essential. Researchers are developing iterative approaches that combine AI predictions with rapid experimental feedback to optimize this process. 

AI and ML are already making significant impacts across the pharmaceutical industry. But what does the future hold for these technologies?

The future of AI and ML in pharma

Pharmacists in lab smiling

AI is revolutionizing drug discovery from target identification to clinical trial design, offering unprecedented speed and efficiency. Companies like Benevolent AI, Insilico Medicine, Recursion, GSK, and Lantern Pharma are using AI to identify novel drug targets, design molecules, and optimize clinical trials. 

While AI shows great potential to reduce drug development time and costs, several challenges remain. The quality and diversity of input data significantly impact the accuracy of AI predictions. Validating AI-identified targets and formulations through experimental methods is crucial, as computational models may not capture all the complexities of biological systems (Vamathevan, 2019). Other challenges include: 

  • Data preparation and integration
  • Intellectual property concerns
  • Lack of skilled personnel with domain-specific knowledge
  • Quality and representativeness of training data
  • AI tool integration with existing pharmaceutical workflows
  • Regulatory considerations for AI-assisted formulation development

Researchers are working to address these limitations by improving data integration techniques and developing more sophisticated AI algorithms that can better handle the intricacies of biological networks (Schneider, 2018).

The integration of AI and ML in pharmaceutical research is not just about replicating human capabilities; it’s about identifying principles that allow agents to act intelligently and improve upon human capabilities. However, not every research question can be answered with AI and ML, particularly if there is high variability, limited data, poor quality of data collection, under-represented patient populations, or flawed trial design (Topol, 2019).

Despite the challenges, generative AI is poised to significantly impact pharmaceutical formulation, leading to more effective and tailored drug products. In the future, the combination of ML (particularly deep learning), with AI, human expertise and experience is likely the best approach to coordinate and analyze the huge and diverse data stores in pharmaceutical research and development (Alam et al., 2023). 

ML and AI are not just buzzwords for the pharmaceutical industry–they’re powerful tools reshaping the entire process of drug discovery and development. From identifying new targets to optimizing lead compounds, AI is accelerating research, which can bring life-saving treatments to patients faster than ever before. While challenges remain, the future of drug discovery looks bright with ML and AI at the helm. 

References

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