The use of AI tools in healthcare is revolutionizing the way we identify, treat, and control illnesses. A new era of personalized medicine, effective clinical workflows, and improved patient outcomes are being made possible by artificial intelligence through the use of big datasets, automation, and predictive analytics.

The most recent advancements and useful uses of AI tools in healthcare, including dose optimization, diagnostics, virtual care, and more, are compiled and arranged in this article.

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AI Tools in Dose Optimization and Therapeutic Drug Monitoring

In order to improve patient safety and treatment results, AI is essential for dose optimization and adverse drug event prediction. Healthcare professionals can lower risks and enhance patient care by using AI algorithms to forecast possible adverse drug events and optimize medication dosages for each patient.

In a study that sought to create a decision support system for optimizing the dosage of warfarin maintenance and an AI-based prediction model for the prothrombin time international normalized ratio (PT/INR), the algorithm outperformed expert physicians with significant differences in predicting future PT/INRs, and the generated customized warfarin dose was dependable, according to the authors’ analysis of data from 19,719 inpatients across three institutions.

Conversely, CURATE.AI is a new dose optimization system that uses AI to optimize chemotherapy dosages dynamically based on patient-specific information. This system was validated as an open-label, prospective trial in patients receiving three distinct chemotherapy regimens for advanced solid tumors.

CURATE.AI used the correlation between tumor marker readouts and chemotherapy dose variation to generate customized doses for subsequent cycles.

Comparing CURATE.AI to the standard of care, the clinical workflow demonstrated successful integration and potential benefits, including a reduction in chemotherapy dose and an improvement in patient response rates and durations.

These results demonstrate the potential of AI in optimizing chemotherapy dosage and reducing the risk of adverse drug events, and they also support the necessity of prospective validation through randomized clinical trials. One method for maximizing medication dosage for specific patients is therapeutic drug monitoring, or TDM.

In order to prevent both toxic levels and under dosing, it is primarily used for medications with a narrow therapeutic index. TDM seeks to minimize side effects while ensuring that patients receive the appropriate medication at the appropriate time and dose in order to achieve the intended therapeutic outcome.

The monitoring and prescribing of medications could be completely transformed by the application of AI in TDM. Based on a person’s genetic composition, medical history, and other variables, AI algorithms can be trained to forecast how they will react to a particular medication.

Better patient outcomes and more effective treatments may result from this individualized approach to medication therapy. Predicting drug-drug interactions with ML algorithms is one application of AI in TDM. These algorithms can detect possible drug interactions by examining sizable patient data sets.

This can lower the chance of negative drug reactions, save money, and enhance patient outcomes. Predictive analytics is another way AI is being used in TDM to identify patients who are at a high risk of experiencing negative drug reactions.

Healthcare professionals can take proactive measures to stop negative events before they happen by evaluating patient data and identifying possible risk factors.

AI Tools and Technological Integration in Healthcare Systems

Some of these supply-and-demand issues may be resolved by using technology and artificial intelligence (AI) in the healthcare industry.

A moment of convergence between healthcare and technology is being heralded by the growing availability of multi-modal data (genomics, economic, demographic, clinical, and phenotypic), as well as technological advancements in mobile, internet of things (IoT), computing power, and data security.

This convergence will radically alter healthcare delivery models through AI-augmented healthcare systems. Specifically, cloud computing is making it possible for safe and efficient AI systems to be incorporated into the delivery of healthcare on a larger scale.

When compared to the traditional on premises infrastructure of healthcare organizations, cloud computing offers the computing capacity for the analysis of significantly larger amounts of data at faster speeds and at a lower cost.

In fact, we see that a growing number of technology companies are looking to collaborate with healthcare institutions in order to promote AI-driven medical innovation made possible by cloud computing and technological transformation.

Current and Future Use Cases of AI Tools in Healthcare

By democratizing and standardizing a future of connected and AI-augmented care, precision diagnostics, precision therapeutics, and, eventually, precision medicine, artificial intelligence (AI) can help healthcare systems fulfill their quadruple aim.

Potential use cases in the healthcare industry (both physical and mental health) include drug discovery, virtual clinical consultation, disease diagnosis, prognosis, medication management, and health monitoring. Research on the application of AI in healthcare is still accelerating quickly.

Connected/Augmented Care

Through the care pathway, artificial intelligence (AI) could improve patient flow and experience, reduce healthcare inefficiencies, and improve caregiver and patient safety.

For instance, AI could be used to remotely monitor patients (e.g., intelligent telehealth through wearables/sensors) in order to identify and promptly care for patients who are at risk of deteriorating.

Virtual Assistants and AI Chatbots

In primary care and community settings, patients are using AI chatbots, like those found in Babylon and Ada, to recognize symptoms and suggest additional steps. Wearable technology, like smartwatches, can be integrated with AI chatbots to give patients and caregivers insights into how to improve their behavior, sleep, and overall health.

Ambient and Intelligent Care

We also note the emergence of ambient sensing without the need for any peripherals.

Automation and Ambient Clinical Intelligence

AI systems that use natural language processing (NLP) technology, like Nuance Dragon Ambient eXperience, have the potential to automate administrative tasks like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up clinicians to spend more time providing patient care.

AI Tools in Precision Diagnostics

Diagnostic Imaging

The most popular AI application at the moment is the automated classification of medical images. More than half (129 (58%) and 126 (53%) of the AI/ML-based medical devices approved in the USA and Europe between 2015 and 2020 were approved or CE marked for radiological use, according to a recent review.

Research has shown that AI can perform as well as or better than human experts in image-based diagnosis across a number of medical specialties, such as radiology (a convolutional neural network trained with labeled frontal chest X-ray images outperformed radiologists in detecting pneumonia), dermatology (a convolutional neural network trained with clinical images classified skin lesions accurately), pathology (one study trained AI algorithms with whole-slide pathology images to detect lymph node metastases of breast cancer and compared the results with those of pathologists), and cardiology (a deep learning algorithm diagnosed heart attack with a performance comparable to that of cardiologists).

Diabetic Retinopathy Screening

Diabetic retinopathy screening and timely treatment are essential to lowering avoidable, diabetes-related vision loss globally. However, considering the large number of diabetic patients and the shortage of eye care personnel globally, screening is expensive.

Strong diagnostic performance and cost effectiveness have been shown in research studies on automated AI algorithms for diabetic retinopathy conducted in the USA, Singapore, Thailand, and India.

Additionally, the Food and Drug Administration-approved AI algorithm IDx-DR, which showed 87% sensitivity and 90% specificity for identifying more-than-mild diabetic retinopathy, was approved for Medicare reimbursement by the Centers for Medicare & Medicaid Services.

Improving the Precision and Reducing Waiting Timings for Radiotherapy Planning

Helping clinicians with image preparation and planning tasks for radiotherapy cancer treatment is a significant use of AI. At the moment, segmenting the images is a tedious and time-consuming process that is done by hand by an oncologist who uses software specifically made for this purpose to draw contours around the areas of interest.

Read Also: The State of AI Adoption in 2024

Waiting times for the initiation of potentially life-saving radiation therapy can be significantly decreased due to the AI-based InnerEye open-source technology, which can reduce this preparation time for prostate and head and neck cancer by up to 90%.

AI Tools in Drug Discovery and Precision Therapeutics

Precision Therapeutics

We must significantly advance our knowledge of disease if we are to move closer to precision therapies. In order to develop digital and biological biomarkers for diagnosis, severity, and progression, researchers from all over the world are investigating the cellular and molecular basis of disease by gathering a variety of multimodal datasets.

AI-Driven Drug Discovery

AI will significantly improve the design of clinical trials and optimize drug manufacturing processes. In fact, AI has the potential to replace all combinatorial optimization processes in the healthcare industry.

Recent announcements from DeepMind and AlphaFold have already signaled the start of this, laying the groundwork for improved comprehension of disease processes, protein structure prediction, and the creation of more specialized treatments (for both uncommon and common diseases).

Empowering Healthcare Professionals with AI Tools

Eventually, medical professionals will use AI to supplement their care, enabling them to deliver safer, more standardized, and more effective care at the highest level of their license.

For instance, clinicians could use a AI digital consult to look at digital twin models of their patients, which are truly digital and biomedical versions of their patients. This would allow them to test the efficacy, safety, and experience of an intervention (like a cancer drug) in a virtual setting before delivering it to the patient in real life

Global Healthcare Impact and Access

Artificial intelligence has the potential to help close the gap in access to healthcare services for 4.5 billion people. AI tools are already assisting medical professionals in identifying fractures, classifying patients, and identifying illness early on.

However, a white paper titled The Future of AI-Enabled Health: Leading the Way from the World Economic Forum claims that the healthcare sector is below average in terms of its adoption of AI when compared to other industries.

Conclusion: AI Tools in Healthcare

Every facet of the medical ecosystem is being transformed by AI tools in healthcare, from virtual care and global health equity to diagnostics and dose optimization.

In addition to changing the way care is provided, these technologies are laying the groundwork for a more accessible, effective, and customized medical future as innovation quickens and adoption broadens.