How will AI be used in healthcare?

Artificial intelligence is poised to revolutionize medicine, with faster diagnoses, more personalized treatments, and higher quality of care. AI also brings significant risks and concerns. Let’s see how AI is reshaping the healthcare industry.

Although AI is only beginning to transform the healthcare sector, artificial intelligence technologies are already making waves in the medical field. AI’s applications in healthcare are already vast, with more uncovered all the time.

Let’s look at some current niche use cases for AI within the healthcare industry.

AI in diagnostics and imaging analysis

Artificial intelligence is revolutionizing medical imaging and diagnostics, enhancing the speed and accuracy of disease detection. AI algorithms can analyze complex medical images such as X-rays, MRIs, and CT scans with remarkable precision, often outperforming human radiologists in certain tasks.

Here are a few examples of AI’s applicability to diagnosis and imaging:

• A 2023 Swedish clinical trial found AI enhanced breast cancer detection in mammography screening. One radiologist plus AI outperformed two radiologists, detecting 4% more cancers with 4% fewer recalls. Artificial intelligence alone matched two radiologists in detection but greatly reduced recalls.
• AI-assisted coronary CT angiography demonstrated high accuracy in diagnosing coronary artery disease in a 2023 community hospital study of 532 patients. The AI system identified significant differences in plaque characteristics and stenosis levels, with results largely matching invasive angiography.
• A 2017 Nature study showed a deep learning AI system, trained on 129,450 clinical images, could classify skin cancer as accurately as board-certified dermatologists. The AI matched expert performance in distinguishing between malignant and benign skin lesions.
• In another melanoma diagnosis study (this one in 2019), a deep learning AI, trained on 12,378 dermoscopic images, outperformed 136 out of 157 dermatologists in classifying melanoma. The AI demonstrated higher specificity at equal sensitivity and higher sensitivity at equal specificity compared to human experts.
• A systematic review of 73 studies found AI systems highly accurate in detecting gastrointestinal luminal pathologies, with 91.9% sensitivity and 91.7% specificity overall.
• A Mayo Clinic study highlighted an AI-assisted screening tool for asymptomatic left ventricular dysfunction. The AI-enhanced tool demonstrated remarkable accuracy, successfully identifying at-risk individuals 93% of the time. This performance surpasses the accuracy of mammograms, which are correct about 85% of the time.
• IDx-DR, developed by University of Iowa researchers, is an AI-based diagnostic system for detecting diabetic retinopathy. Approved by the FDA in 2018, it's the first AI system authorized to diagnose eye disease without clinician interpretation. The system uses a fundus camera to capture retinal images, which are then analyzed by AI algorithms. It detects more than mild diabetic retinopathy with 87.4% sensitivity and 89.5% specificity.
• Researchers at the University of California, San Francisco, have created an AI model that can detect Alzheimer's disease six years before a clinical diagnosis, analyzing brain scans to identify patterns invisible to the human eye.

AI in clinical decision support

Artificial intelligence is increasingly being used to support clinical decision-making, helping medical professionals navigate complex patient data and treatment options. AI systems can analyze vast amounts of medical literature, patient records, and clinical guidelines to provide evidence-based recommendations in real-time.

For example, IBM's Watson for Oncology analyzes patient medical records and compares them against a vast database of medical literature to suggest personalized treatment plans for cancer patients. In a 2019 study, Watson's treatment recommendations concurred with those of oncologists in 93% of breast cancer cases.

Dr. Eric Topol, a cardiologist and digital medicine researcher, notes that "AI can help doctors with many of the data-heavy parts of their job, such as interpreting scans, streamlining paperwork and even analysing patients' voices for signs of conditions such as depression or heart disease."

AI is also proving valuable in predicting patient outcomes and identifying high-risk individuals. A Northwestern Medicine study demonstrates AI's superior ability in predicting breast cancer outcomes compared to expert pathologists. The AI analyzes 26 tissue properties, including non-cancerous elements, to generate comprehensive prognostic scores.

AI in drug discovery and development

Artificial intelligence is accelerating drug discovery and development processes that traditionally take years and billions of dollars. AI algorithms can analyze vast databases of molecular structures, predict drug-target interactions, and even design novel compounds, potentially bringing life-saving medications to market faster and more cost-effectively.

Here are some areas where AI is already making an impact in drug discovery, development, and testing:

1. Target identification: AI can analyze genomic data and scientific literature to identify new drug targets.
2. Compound screening: machine learning models can predict which compounds are likely to be effective against a target, reducing the need for extensive lab testing and cutting down the drug discovery process timeline.
3. Drug design: AI can generate and optimize new molecular structures, creating potential drug candidates that human researchers might not have conceived.
4. Predicting drug properties: AI models can forecast a compound's pharmacokinetics and potential side effects early in the development process.
5. Clinical trial optimization: AI can help design more efficient clinical trials and predict which patients are most likely to respond to a treatment.

For example, in 2020, British startup Exscientia and Japanese pharmaceutical company Sumitomo Dainippon Pharma used AI to develop a drug for obsessive-compulsive disorder that entered human clinical trials in just 12 months—a process that typically takes 4-5 years.

A recent analysis of AI-native biotech companies' clinical pipelines revealed that AI-discovered molecules achieved an impressive 80-90% success rate in Phase I trials, substantially outperforming industry averages.

AI in personalized treatment planning

Artificial intelligence is enabling a greater degree of personalization in the creation of individual treatment plans. By analyzing vast amounts of patient data, including genetic information, medical history, lifestyle factors, and even social determinants of health, AI can help clinicians tailor treatments to individual patients with unprecedented precision.

1. Genomic analysis: AI can rapidly interpret genetic data to identify potential disease risks and drug responses.
2. Treatment optimization: AI algorithms can predict which treatments are likely to be most effective for a specific patient.
3. Dosage adjustment: machine learning models can suggest optimal drug dosages based on individual patient characteristics.
4. Monitoring and adaptation: AI can continuously analyze patient data to recommend treatment adjustments in real-time.

Case study: oncology In cancer treatment. MIT researchers developed an AI system that predicts breast cancer risk up to five years in advance by analyzing mammograms and patient histories. This deep-learning model outperforms traditional risk assessment methods, accurately identifying 31% of cancer patients in its highest-risk category. The system aims to enable tailored screening and prevention programs, moving away from one-size-fits-all approaches. It performs equally well for white and black women, addressing racial disparities in current tools.

Case study: diabetes treatment. In a 2017 study, researchers developed a k-nearest neighbor algorithm using electronic medical records from over 10,000 patients to provide tailored treatment recommendations for type 2 diabetes. The AI's suggestions differed from standard care in 31.8% of cases, leading to a mean HbA1c reduction of 0.44% in these instances and 0.14% overall. The algorithm showed benefits across different demographic groups, particularly for patients under 60 and those with poor glycemic control.

AI in remote patient monitoring and telehealth

Artificial intelligence is transforming remote patient monitoring and telehealth, enabling more efficient and effective healthcare delivery outside of a brick-and-mortar clinical practice. AI-powered systems can continuously analyze patient data, identify trends, and alert healthcare providers to potential issues before they become critical.

Check out some of these applications for AI in telehealth and monitoring:

1. Chronic disease management: AI algorithms can monitor vital signs and symptoms for patients with conditions like diabetes, heart disease, or COPD, providing early warnings of deterioration.
2. Post-operative care: AI-enabled remote monitoring can track recovery progress and flag potential complications after surgery.
3. Mental health support: AI chatbots and mood analysis tools can provide ongoing support and monitoring for patients with mental health conditions.
4. Medication adherence: smart pill dispensers with AI can track and encourage medication compliance.
5. Triage and diagnosis: AI-powered symptom checkers can help prioritize telehealth consultations and guide initial diagnoses.

For example, an AI-enabled digital stethoscope significantly outperformed on-site human diagnosis, detecting 94.1% of heart valve disease cases compared to 41.2% by healthcare professionals using conventional stethoscopes. The digital stethoscope identified 22 previously undiagnosed cases of moderate-or-greater heart valve disease.

The same report highlighted a deep learning algorithm that analyzed retinal images of diabetic patients. The algorithm effectively categorized patients with prediabetes or Type 2 diabetes into cardiovascular risk groups. The high-risk group identified by AI showed an 88% higher likelihood of experiencing cardiovascular events compared to the low-risk group.

AI in healthcare administrative tasks and workflow optimization

If healthcare professionals have more time to focus on patients, we should expect better health outcomes. AI has the potential to free doctors up from menial administrative tasks so they can do the job they went to med school for. By automating routine tasks and optimizing workflows, AI can improve efficiency and reduce costs for healthcare organizations.

Here are a few of the admin applications that AI is already doing or will be doing in the near future for health systems:

• Automated scheduling: AI algorithms can optimize appointment scheduling, reducing wait times and improving resource utilization.
• Medical coding and billing: AI-powered systems can accurately code medical procedures and diagnoses, speeding up billing processes and reducing errors.
• Claims processing: machine learning models can detect fraudulent claims and expedite legitimate ones, improving financial efficiency.
• Patient flow management: AI can predict patient volumes and optimize staff allocation, reducing bottlenecks in hospitals and clinics.
• Documentation and data entry: natural language processing (NLP) can transcribe and summarize medical notes, reducing administrative burden on healthcare providers and automating some of the most repetitive tasks in healthcare.

Dr. Robert Wachter, Chair of the Department of Medicine at UCSF, notes of the role of AI in healthcare: "Some of the greatest gains could come from tools that free up back-office functions, help summarise a lengthy patient record, or create a high-quality physicians’ note from listening to a conversation, allowing doctors to concentrate on talking to the patient rather than typing on a keyboard."

Here's the revised version with the first word of every sentence capitalized:

AI for medical devices

Artificial intelligence is merging with medical devices. It’s enabling more accurate diagnostics, continuous monitoring, and personalized treatment. AI-enabled devices are enhancing patient care across diverse medical specialties and healthcare settings.

• Wearable health monitors: AI algorithms in smartwatches and fitness trackers can detect irregular heart rhythms and predict health events.
• Smart insulin pumps: AI-powered systems can predict blood glucose levels and automatically adjust insulin delivery for diabetic patients.
• AI-enhanced imaging devices: MRI and CT scanners with integrated AI can produce clearer images and assist in identifying abnormalities.
• Neurostimulation devices: AI algorithms can optimize deep brain stimulation for conditions such as Parkinson's disease and can adjust in real-time to patient needs.
• AI-powered prosthetics: advanced prosthetic limbs use AI to interpret nerve signals and provide more natural movement.