Tailored Treatments: The Power of AI in Personalized Medicine

With advancements in modern medicine, “one-size-fits-all” treatment plans are quickly becoming outdated. As artificial intelligence (AI) becomes more incorporated into all parts of daily life, healthcare is set to transition towards more personalized and precise approaches, tailoring treatment to the unique genetic makeup and conditions of the individual patient. 

For the purposes of this article, patient care will be examined through the dual lenses of diagnosis and treatment. While these categories encompass various sub-categories and specific processes, they represent the primary areas where AI can have the most significant impact.

Diagnosis can be understood as the initial intake of the patient, including imaging, lab tests, and the assessment of symptoms. It is the phase where AI can assist in accurately identifying diseases through advanced data analysis and pattern recognition.

Treatment, on the other hand, involves determining the appropriate interventions based on the diagnosis. This can range from deciding if any intervention is necessary to outlining specific treatment steps. If intervention is deemed necessary, AI can further guide the selection of targeted therapies, predict treatment outcomes, and monitor patient progress to adjust treatments as needed.

Improving Diagnosis

Diagnosis is a critical step in patient care, involving the initial intake, imaging and assessment of symptoms to identify diseases accurately. AI has emerged as a transformation tool in this domain with one of the most apparent applications being in radiology. 

A notable study conducted by Radboud University Medical Center demonstrated the capabilities of AI in diagnosing prostate cancer from MRI scans. In this study, multiple AI development teams competed against radiologists to detect prostate cancer. The AI systems outperformed the radiologists by identifying 7% more significant prostate cancers and identifying suspicious areas that were later not found to be cancer 50% less often than the radiologists. In accurately identifying non-suspicious areas, unnecessary biopsies would be performed less often. While biopsies are objectively the best way to diagnose a disease, an unnecessary procedure creates inefficiency within the healthcare system and places more strain on healthcare professionals and patients. Given that burnout and feeling overworked are common complaints amongst medical professionals, the AI system not only helps the patient, but also those responsible for taking care of the patient.

Another major benefit of AI, particularly in radiology, is the significant reduction in the time it takes to make a diagnosis from when the scan is produced. Traditionally, patients often have to wait several days to receive results from an MRI or CT scan, pending a radiologist’s review. With AI, same day results could be delivered, reducing patient waiting time and accelerating the process toward intervention.

The electrocardiogram (ECG) is a medical test that records the electrical activity of the heart over a period of time. Various heart conditions, such as arrhythmias, and other cardiovascular disorders are commonly diagnosed with the help of ECGs. ECGs are notoriously one of the most difficult subjects for medical students to master despite their prevalent use in clinical settings.

Given that analyzing an ECG is primarily pattern recognition of a finite dataset, AI models trained on previous clinical datasets have the potential to be extremely accurate; already proving so in a study in which one model “outperformed cardiologists for some diagnoses.” (Siontis, 2021)

As one’s heartbeat is (hopefully) continuous, the continuous monitoring of ECGs by AI systems provides potential for diagnoses to be made without the supervision of a physician. Smart watches are being studied as potential candidates for providing information to systems that could detect silent atrial fibrillation. While increased detection capabilities are great, the volume of AI accumulated and utilized data that medical professionals would have to sort through and analyze would increase substantially. Similar to the applications in radiology, AI systems address this issue by performing analyses that can be relayed to a physician for review and the formulation of a treatment plan.

Treatment

Access to information is crucial for formulating and developing effective treatment plans in patient care. Physicians must complete years of rigorous coursework, exams, and clinical programs to gain the expertise required to treat patients. The sheer volume of medical data a physician is expected to know and access is vast and constantly growing. When a patient walks into a doctor’s office with a variety of potential medical issues, it is not only the role of the physician to sift through symptoms to provide an accurate diagnosis, but also to develop an appropriate treatment plan. AI can aid physicians in the process of producing an appropriate treatment plan by accessing large amounts of data quickly and accurately. One particular area in which AI has the potential to excel is in optimizing drug dosages.

While physicians optimize drug dosages by considering patient-specific factors such as age, weight, genetics, and previous responses to treatment, AI can significantly enhance this process by leveraging extensive datasets. Although comprehensive databases of drug interactions do exist, AI can analyze this information more effectively and efficiently. This personalized approach not only maximizes the effectiveness of medications but also minimizes the risk of adverse effects, especially when multiple medications are administered simultaneously.

With over 20,000 FDA-approved drug products, it is challenging for physicians to fully understand the interactions between different drugs without research and assistance. An AI-powered monitoring system can continuously assess and identify rare drug-drug interactions, ensuring safer and more effective treatment plans for patients. Additionally, AI can potentially dynamically adjust dosages based on real-time information, alleviating some of the stress on physicians and allowing them to focus on other critical aspects of the treatment protocol. This not only enhances patient health outcomes but also optimizes a physician’s time, along with the overall efficiency of the healthcare system.

An application outside the doors of a hospital where dynamically adjusting dosages can significantly alleviate stress for individuals with health conditions is in the use of insulin pumps for diabetes management. Diabetes is a chronic condition that affects millions of people worldwide and requires continuous monitoring and careful management of blood glucose levels. One of the most challenging aspects of diabetes management is determining the correct insulin dosage, which is currently managed by patients manually.

AI systems that can monitor blood glucose levels and make real-time adjustments to administer the correct amount of insulin can greatly reduce the burden on individuals with diabetes. When the AI system handles these adjustments automatically, it increases the likelihood of patient compliance, as the individual no longer needs to manually manage their insulin dosages. This increased compliance leads to more effective treatment and improved overall health for the patient, ultimately resulting in fewer visits to healthcare professionals and reducing strain on the healthcare system. A win-win for everyone involved.

Outlook

The integration of AI is revolutionizing healthcare by enabling personalized and precise treatment plans. From improving diagnostic accuracy in radiology and ECG analysis to optimizing drug dosages and managing chronic conditions like diabetes, AI enhances patient outcomes and reduces the burden on healthcare professionals, leading to more efficient and effective healthcare.

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