AI in Clinical Trials: Improving Drug Development and Patient Care

AI in Clinical Trials: Improving Drug Development and Patient Care

AI Health Tech Med Tech

The landscape of clinical trials is quickly evolving, with artificial intelligence (AI) playing an increasingly pivotal role. The number of AI-driven firms specializing in drug discovery and development has grown from 62 in 2011 (Sokolova, 2023) to 400 firms in 2022.

This shift is not just about cutting-edge technology; it’s about improving lives and bringing new treatments to patients faster than ever before. Let’s dive in and see how AI in clinical trials works in healthcare.

Contents

The Current State of AI in Clinical Trials

Clinical trials are the most robust way to show the safety and effectiveness of a treatment or clinical approach, and provide evidence to guide medical practice and health policy. Unfortunately, they have a high failure rate.

Current clinical trials are complex, labor-intensive, expensive, and may involve errors and biases (Zhang et al., 2023). They often start late in the drug development cycle. Only around 10% of drugs entering the clinical trial stage get approved by the U.S. Food and Drug Administration (FDA) [Mai et al., 2023]. 

Key areas where AI is used in clinical trials include:

  • Patient recruitment and retention
  • Trial design and protocol optimization
  • Data management and analysis
  • Safety monitoring and detection of adverse drug reactions (ADRs)
  • Drug discovery and development

According to McKinsey, AI adoption could boost up to $25 billion into clinical development within the pharmaceutical industry, with the potential to a total gain of $110 billion (Bhavik et al., 2024).

Beyond recruitment, AI is also revolutionizing how clinical trials are designed and conducted.

Improving Patient Engagement with AI 

Doctor and patient POCs

Traditional clinical trial methods often face challenges like slow patient recruitment, high dropout rates, and inefficient data analysis. AI is helping to address these issues by providing faster, more accurate, and more personalized solutions (Hutson, 2024). 

Patient Recruitment

Traditional clinical trials have an average 30% dropout rate due to inconvenience, complex protocols, and lack of support (Clinical Trials Arena, 2024). Another big hurdle in clinical trials is finding the right patients, in part due to (Atieh & Domanska, 2024):

  • Lack of eligible participants
  • Inadequate patient awareness
  • Limited locations 

AI is changing the game by:

  • Analyzing electronic health records (EHRs) to identify suitable candidates
  • Using predictive analytics to improve patient retention rates
  • Creating personalized communication strategies to keep patients engaged

For example, AI algorithms can sift through huge amounts of patient data to find those who meet specific trial criteria. Clinical trial matching systems or services use natural language processing (NLP) tools that learn clinical trial protocols and patient data. This process makes recruitment faster, and helps ensure a more diverse and representative patient population (Zhang et al., 2023).

Patient Retention

The majority of clinical trials have participants who drop out. AI can improve retention by (Mai et al., 2023):

  • Identifying factors associated with a high risk of dropping out
  • Predicting the probability that a participant will drop out

AI-powered chatbots are also playing a crucial role in maintaining continuous communication with trial participants by:

  • Providing support 
  • Sending reminders (via AI-assisted apps) [Clinical Trials Arena, 2024]
  • Tracking progress
  • Responding to various events and milestones during the trial 

This personalized engagement can help foster a positive patient experience and build trust, which is crucial for patient retention (Jackson, 2024).

Enhanced Trial Design with Digital Health Technologies (DHTs)

Two researchers looking at a Mac

Decentralized clinical trials (DCTs) can incorporate DHTs to streamline trial design, and expand where to conduct them. 

DHTs aren’t just wearable trackers. It’s possible to implant, swallow, or insert many DHTs into the body. Placing DHTs in a particular setting with real-time data capture from trial participants in their homes and other locations makes it more convenient for them. It also gives clinicians insights on patient health status and healthcare delivery (U.S. Food & Drug Administration, 2024).

As trial designs become more sophisticated, AI can simplify managing and analyzing the resulting data.

AI can make clinical trials more efficient and effective:

  • AI-assisted trial design helps researchers create more robust study protocols
  • Adaptive trial designs use real-time data analysis to make adjustments on the fly
  • Machine learning optimizes inclusion and exclusion criteria for diverse patient selection

These AI-powered approaches can lead to faster, more cost-effective trials with higher success rates.

Data Management and Analysis in Clinical Trials with AI

Group of 4 researchers in a meeting

With decentralized clinical trials, teams must collect data from different sources including (Informatica):

  • Various types of EHRs
  • Data from providers and medical facilities
  • Wireless medical devices that may exist in professional settings or patients’ homes.

In the age of big data, AI is an invaluable tool for managing and analyzing the vast amounts of information generated during clinical trials:

  • AI systems can process and integrate data from multiple sources
  • Real-time data monitoring ensures quality control throughout the trial
  • AI-driven insights enable faster decision-making for researchers and clinicians

By harnessing the power of AI, researchers can uncover patterns and insights that might otherwise go unnoticed. For instance, AI can extract data from unstructured reports, annotate images or lab results, add missing data points, and identify subgroups among a population that responds uniquely to a treatment (Clinical Trials Arena, 2024).

Improving Safety Monitoring and Adverse Event Detection

Monitor attached to back of a woman's left shoulder

Patient safety is paramount in clinical trials. AI is enhancing pharmacovigilance (drug safety) efforts by:

  • Using algorithms for early detection of adverse events
  • Creating predictive models to assess patient safety risks
  • Automating safety signal detection and analysis

These AI-powered tools can help researchers identify potential safety issues faster and more accurately than traditional methods.

While efficient data management is crucial, ensuring patient safety remains paramount in clinical trials.

Accelerating Drug Discovery and Development

Researcher looking at microcope with several vials in foreground

The typical amount of time to launch a new drug is 10 to 12 years. The clinical trial stage itself averages five to seven years (Shah-Neville, 2024).

The estimated cost of launching a new drug is roughly $2.6 billion. Delays in time to market make drug development expensive.

AI isn’t just changing how we conduct clinical trials – it’s also speeding up the entire drug development process:

  • AI assists in target identification and validation for new drugs
  • Machine learning predicts drug efficacy and toxicity
  • AI-powered simulations reduce time and costs in the development pipeline

By leveraging AI, pharmaceutical companies can bring new treatments to patients faster and more efficiently.

As we embrace AI’s potential, we must also address the ethical and regulatory challenges it presents.

Ethical Considerations and Regulatory Challenges

Doctor and patient hands on desk 2

As with any new technology, AI can return inaccurate data or misinterpret nuances in informed consent documents or clinical trial protocols, emphasizing the need for human review (Nonnemacher, 2024).

The use of AI in clinical trials also raises important ethical and regulatory questions:

  • How do we ensure data privacy and security in AI-driven trials?
  • What steps can we take to address bias in AI algorithms and datasets?
  • How should regulatory frameworks evolve to accommodate AI integration in clinical research?

These are complex issues that require ongoing dialogue between researchers, ethicists, regulators, and patients as described in other AI health articles I’ve covered.

As AI technology continues to advance, we can expect to see even more innovative applications in clinical research. 

The Future of AI in Clinical Trials

Group of researchers in a clinical trial

What does the future hold for AI in clinical trials? Some exciting possibilities include:

  • Virtual clinical trials that reduce the need for in-person visits
  • AI systems that collaborate with human researchers to design better studies
  • Precision medicine approaches tailored to individual patients based on AI analysis

Industry experts predict continued growth in AI adoption, with a focus on identifying the most beneficial areas for AI implementation in clinical trials (Studna, 2024).

Conclusion

AI is proving to be an invaluable tool in the clinical research toolkit, offering new ways to streamline processes, improve patient experiences, and accelerate drug development. 

But AI is not a magic solution; it’s a powerful assistant that works best when combined with human expertise and ethical considerations. 

The synergy between AI and clinical trials holds immense promise for advancing medical research, developing more effective treatments, and ultimately, improving patient outcomes. The journey of AI in clinical trials is just beginning, and the potential for positive impact on global health is boundless. 

What do you think about the role of AI in clinical trials? Are you optimistic about its potential to improve patient care?

References

Atieh, D. & Domanska, O. (2024). Finding the right patients for the right treatment with AI. Avenga. Retrieved from https://www.avenga.com/magazine/how-ai-advances-patient-recruitment-in-clinical-trials

Bhavik Shah, B., Bleys, J., Viswa, C.A., Zurkiya, D., & Eoin Leydon, E. (2024). Generative AI in the pharmaceutical industry: Moving from hype to reality. McKinsey. Retrieved from https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality

How AI data management can transform your clinical trial. Clinical Trials Arena. 

Retrieved from https://www.clinicaltrialsarena.com/sponsored/how-ai-data-management-can-transform-your-clinical-trial/

Hutson, M. (2024). How AI in being used to accelerate clinical trials. Nature; 627(S2-S5). doi.org/10.1038/d41586-024-00753-x

Informatica. (n.d.) Using AI and Data Management to De-Risk Decentralized Clinical Trials. Retrieved from https://www.informatica.com/resources/articles/ai-data-management-decentralized-clinical-trials.html

Jackson, R. (2024). 3 Areas Where AI Could Revolutionize Patient Recruitment and Retention. Clinical Leader. Retrieved from  https://www.clinicalleader.com/doc/areas-where-ai-could-revolutionize-patient-recruitment-and-retention-0001

Mai, B., Roman, R., & Suarez, A. (2023). Forward Thinking for the Integration of AI into Clinical Trials. Clinical Researcher; 37(3). Retrieved from  https://acrpnet.org/2023/06/forward-thinking-for-the-integration-of-ai-into-clinical-trials

Nonnemacher, H. (2024). Two years of AI learning: Streamlining clinical trials today for future advancements. Suvoda. Retrieved from https://www.suvoda.com/insights/blog/two-years-of-ai-learning

President’s Cancer Panel. (2018). Part 1: The Rising Cost of Cancer Drugs: Impact on Patients and Society. Retrieved from https://prescancerpanel.cancer.gov/report/drugvalue/Part1.html

Sha-Neville, W. (2024). How AI is shaping clinical research and trials. Labiotech. Retrieved from  https://www.labiotech.eu/in-depth/ai-clinical-research

Sokolova, S. (2023). 12 Notable AI-powered Biotech Companies Founded in 2021. BioPharmaTrend. Retrieved from https://www.biopharmatrend.com/post/500-10-notable-ai-powered-biotech-companies-founded-in-2021

Studna, A. (2024). Future Use of Artificial Intelligence in Clinical Trials. Applied Clinical Trials. 

Retrieved from https://www.appliedclinicaltrialsonline.com/view/future-artificial-intelligence-clinical-trials

U.S. Food & Drug Administration. (2024). The Role of Artificial Intelligence in Clinical Trial Design and Research with Dr. ElZarrad. Retrieved from

https://www.fda.gov/drugs/news-events-human-drugs/role-artificial-intelligence-clinical-trial-design-and-research-dr-elzarrad

Zhang, B., Zhang, L., Chen, Q., Jin, Z., Liu, S., & Zhang, S. (2023). Harnessing artificial intelligence to improve clinical trial design. Communications Medicine, 3(1), 1-3. doi.org/10.1038/s43856-023-00425-3 

Personalized Healthcare: The Role of AI in Precision Medicine

Personalized Healthcare: The Role of AI in Precision Medicine

AI Med Tech

Have you ever wondered how your unique genetic makeup, lifestyle, and environment influence your healthcare? 

Welcome to the world of AI in personalized medicine, also known as precision medicine, where AI is playing a pivotal role in tailoring treatments to individual patients. In this article, we’ll explore how AI is changing the way we approach individual patient care, from diagnosis to treatment and beyond.

Contents

What is Precision Medicine?

Precision medicine aims to provide tailored healthcare solutions based on an individual’s genetic, environmental, and lifestyle factors. 

Understanding AI in Precision Medicine

3 researchers in a lab smiling

AI enhances personalized healthcare approaches by analyzing vast amounts of data to identify patterns and make predictions. It’s like having a super-smart assistant that can process information much faster and more accurately than humans. 

Subsets of AI driving changes in healthcare

The key technologies driving AI in healthcare include:

  • Machine learning: Algorithms that learn from data and improve over time
  • Deep learning: A subset of machine learning that uses neural networks to mimic human brain function
  • Natural language processing: The ability of computers to understand and interpret human language

These technologies work together to process complex medical data, leading to more accurate diagnoses and personalized treatment plans.

AI-Powered Diagnostics and Disease Prediction

One of the most exciting applications of AI in precision medicine is its ability to improve diagnostics and predict diseases. Here’s how.

Early detection of diseases

AI algorithms can analyze patient data to find subtle signs of diseases before they become apparent to human doctors. For example, researchers have developed AI models that can detect early signs of Alzheimer’s disease up to six years before a clinical diagnosis (Grassi et al., 2018).

Medical imaging analysis

MRI machine with brain scans on the side

AI is particularly adept at analyzing medical images like X-rays, MRIs, and CT scans. In some cases, AI algorithms have shown higher accuracy than human radiologists in detecting certain conditions. A study published in Nature found that an AI system outperformed human experts in breast cancer detection, reducing both false positives and false negatives (McKinney et al., 2020).

Predictive models for disease risk assessment

By analyzing a patient’s genetic data, lifestyle factors, and medical history, AI can create predictive models to assess an individual’s risk for various diseases. This allows healthcare providers to implement preventive measures and early interventions.

Tailoring Treatment Plans with AI

AI isn’t just helping with diagnostics; it’s also revolutionizing how we approach treatment. 

AI-assisted drug discovery and development

AI is accelerating the drug discovery process by:

  • Analyzing molecular structures to predict potential drug candidates
  • Simulating drug interactions to identify potential side effects
  • Optimizing clinical trial designs for faster and more efficient testing

Personalized treatment recommendations

Female doctor showing her elderly female patient a tablet

AI algorithms can analyze a patient’s unique characteristics to recommend the most effective treatment options. This includes considering factors like:

  • Genetic profile
  • Medical history
  • Lifestyle factors
  • Environmental influences

Optimizing dosages and reducing adverse drug reactions

AI can help determine the optimal drug dosage for each patient, considering factors like age, weight, kidney function, and potential drug interactions. This personalized approach can significantly reduce the risk of adverse drug reactions.

Genomics and AI: A Powerful Combination

The integration of AI and genomics is opening up new frontiers in personalized medicine. Here’s how.

AI in genomic sequencing and analysis

AI algorithms can quickly analyze large amounts of genomic data, finding patterns and variations that might be missed by human researchers. This accelerates our understanding of genetic factors in disease development and treatment response.

Identifying genetic markers for personalized treatment

genetic markers

By analyzing genetic data, AI can identify specific markers associated with disease risk or treatment response. This information helps healthcare providers customize treatments to a patient’s genetic profile.

Predicting drug responses based on genetic profiles

AI models can predict how a patient might respond to specific medications based on their genetic makeup. This approach, known as pharmacogenomics, helps doctors choose the most effective drugs with the least potential for side effects.

AI in Patient Monitoring and Care Management

AI is also changing how we monitor and manage patient health.

glucose monitor on arm with phone app showing glucose level

Real-time health monitoring using wearable devices and AI

Wearable devices combined with AI algorithms can continuously monitor vital signs and alert healthcare providers to potential issues. For example, AI-powered smartwatches can detect irregular heart rhythms and notify users of potential heart problems (Perez et al., 2019).

Personalized lifestyle and wellness recommendations

AI can analyze data from wearables, along with other patient information, to provide personalized recommendations for diet, exercise, and other lifestyle factors that impact health.

AI virtual health assistants and chatbots

Virtual health assistants and chatbots can provide 24/7 support to patients, answering questions, reminding them to take medications, and even conducting initial symptom assessments.

Challenges and Ethical Considerations

While AI in precision medicine offers tremendous potential, it also presents several challenges

Equitable access to precision medicine

There’s a risk that AI-driven precision medicine can make healthcare disparities worse if it’s not accessible to all populations. Accessible healthcare should be a priority in health systems to ensure these technologies are available to everyone, regardless of socioeconomic status.

For example, a Google Health project tested an AI system for diabetic retinopathy screening in Thailand (Johnson et al., 2021). Despite high accuracy in lab tests, the system faced challenges in actual clinics, such as poor image quality, slow internet, and patient travel issues. This shows the importance of testing AI in real clinical environments and improving systems based on user feedback. However, getting this feedback in healthcare can be time-consuming and expensive. Researchers are exploring alternatives like creating fake data or using simulations to develop better AI systems for healthcare.

Bias in AI algorithms

AI algorithms can inadvertently perpetuate biases present in training data. It’s crucial to develop diverse datasets and implement checks to ensure AI systems provide fair and equitable recommendations across all patient populations.

Data privacy and security concerns

As AI relies on vast amounts of personal health data, ensuring the privacy and security of this information is paramount. Healthcare providers and technology companies must implement robust safeguards to protect patient data.

As AI continues to advance, expect to see more exciting changes we can personalize healthcare.

  • Integration of multi-omics data (genomics, proteomics, metabolomics) for more comprehensive patient profiles
  • Advanced natural language processing for better interpretation of medical literature and clinical notes
  • Quantum computing applications in drug discovery and genomic analysis

Integration of AI in medical education and practice

Hands turning a page in anatomy book

As AI becomes more prevalent in healthcare, medical education will need to evolve to ensure healthcare professionals are equipped to work with AI systems effectively. Healthcare professionals, technologists, and policymakers must collaborate to harness the full potential of AI in precision medicine, ensuring that AI advancements benefit all patients.

Potential impact on healthcare systems and patient outcomes

AI has the potential to:

  • Improve diagnostic accuracy and speed
  • Reduce healthcare costs through more efficient resource allocation of clinical staff
  • Enhance patient outcomes through personalized treatment plans

AI is reshaping precision medicine by providing data-driven insights and tailored treatment plans. While challenges remain, the potential benefits for patient outcomes are limitless. From more accurate diagnostics to custom treatment plans, AI is empowering healthcare providers to deliver truly individualized care that can dramatically improve our quality of life. 

As we continue to refine and expand the ways we use AI in healthcare, we move closer to a future where truly personalized medicine is the norm rather than the exception.

References

Grassi, M., Loewenstein, D. A., Caldirola, D., Schruers, K., Duara, R., & Perna, G. (2018). A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach. International Psychogeriatrics, 30(11), 1755-1763.

Johnson K.B., Wei W.Q., Weeraratne D., Frisse M.E., Misulis K., Rhee K., Zhao J., & Snowdon J.L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Sciences; 14(1):86-93. doi: 10.1111/cts.12884

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

Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., … & Turakhia, M. P. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909-1917.

Streamlined Medical Practice with Ambient Clinical Intelligence

Streamlined Medical Practice with Ambient Clinical Intelligence

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.