AI in Healthcare: From Diagnosis to Treatment – Innovations
Artificial Intelligence (AI) is changing every aspect of the healthcare ecosystem – ranging from how we diagnose diseases, to how we deliver and monitor treatments. But, what does this transformation look like in practice?
This blog aims at how AI is enhancing healthcare efficiency, accuracy, and access, and what professionals, learners, and organizations should look to understand to stay ahead.
The Role of AI in Early Diagnosis
Timely diagnosis is essential in healthcare, and AI is being used to support the doctors in making that diagnosis faster and more accurately:
Imaging and Radiology
AI algorithms are trained to find patterns in images (e.g., X-rays, MRIs, and CT scans). They can diagnose conditions such as:
AI models, such as the lung cancer detection model developed by Google Health, use low-dose CT images to determine malignant nodules with an accuracy of 94.4%, usually on par or better than a radiologist.
Algorithms developed by Zebra Medical Vision, can identify bone fractures in X-ray images (e.g., wrist, hip or spine) with an accuracy of 90-95% by identifying discontinuities in bony structures.
Aidoc is another AI platform that is able to detect brain hemorrhages using CT scans with a sensitivity of 95% in internal bleeding leading to stroke symptoms. In cases of degenerative disease (such as Alzheimer), AI models identify hippocampal atrophy through MRI scans to assist with early diagnosis.
Example: Stanford’s model, CheXNeXt, was able to imitate the expert level of radiology in a trial to identify pneumonia in chest X-rays, where it obtained >90% sensitivity by looking for patterns such as ground-glass opacities.
Predictive Health Models
Machine learning models can evaluate information about a patient’s medical histories and genomic profiles to estimate the risk of becoming ill from a wide variety of diseases; thereby physicians can develop mechanisms of preventive care even before symptoms develop.
AI Chatbots for Preliminary Assessment
AI chatbots subsequently help patients assess their symptoms and determine what next steps to take.
Examples,
- Buoy Health – Uses AI to suggest potential conditions and next steps
- Infermedica – Powers symptom checking and triage decisions for healthcare systems
Why Is AI Driving the Shift Toward Personalized and Precision Medicine?
A personalized approach to care is now growing in favor of a one-size-fits-all approach. AI enables a customized, personalized approach to patient care by considering:
AI looks at a person’s genomic blueprint and understands how their DNA may predict disease risk, and how they may respond to treatments. This action permits the treatment provider to choose therapies that are much more likely to be successful for that specific patient, which is the core framework of precision medicine.
AI models are able to leverage a patient’s treatments over the course of their medical history to identify trends. This means that AI models will identify the therapies that have a greater likelihood of working, or failing. This information is essential to avoid ineffective or harmful treatments and allows for more timely, evidenced based decision-making.
AI models are assessing co-morbidities and conditions (like diabetes, kidney disease, hypertension, etc.) when issues are recommending treatments. Doing this, the AI model minimizes risks, minimizes adverse drug interactions, and ultimately offers treatment plans that are safer and take the care of the complex patient into account.
AI tools can assist healthcare practitioners to match patients with the drug associated with the best clinical effect given their biology.
AI-assisted robots provide surgeons with an added precision during complex procedures including spinal surgeries and heart valve repair.
Drug discovery has typically taken an average of 10–15 years from conception to available for sale. AI is changing this timeline by:
- Identifying candidate molecules
- Simulating drug interactions
- Prioritizing their likelihood of success in a clinical trial
Example: In 2014, as the Ebola outbreak intensified, Atomwise used its AtomNet AI platform to carry out accelerated virtual screening of millions of compound-protein interactions. Within days of screening, AtomNet identified 17 molecules that might disrupt Ebola’s ability to enter human cells. Two of these candidates were shown in lab studies to effectively blockade Ebola, which represents a landmark in drug discovery because it demonstrated how AI can compress years of drug discovery into days (as per oreilly.com).
“Here’s what users on Quora are saying about AI in healthcare.”
AI in Post-treatment and Patient Monitoring
Once the treatment has started, there’s more value added by AI via real time remote monitoring and virtual health platforms.
Wearables:
Smart devices that monitor, for example:
They alert a patient’s doctors in real-time if they spot anything abnormal.
Virtual Care Assistants
AI Bots help the patient:
- Get mental health management when needed
AI in Mental Health
Woebot is an app that provides AI-enabled CBT therapy 24/7, and has been particularly helpful in areas where therapists are in short supply.
Challenges and Considerations in AI-Enabled Healthcare
Although the introduction of AI in health care is undoubtedly exciting, there will still be challenges to address.
Data Privacy
AI systems rely on sensitive data, and as such have to comply with sediment and protection laws, like HIPAA (US) and GDPR (Europe), to protect the privacy of patient data.
Bias and fairness
AI systems that are trained on biased data risk being unequal to other people, especially minorities or minorities.
Explainable AI (XAI)
Doctors want to know what decisions that AI made, and regulators also want information. Why did it make that decision? To build a trust relationship, the machine learning solutions of the future are going to have to be explainable.
Future Predictions for AI in Healthcare:
- Digital Twins: AI models that predict how a patient will respond to various treatments.
- AI + Genomics: Integrating AI with genomic sequencing to treat rare diseases.
- Generative AI in Healthcare: Applications of tools like ChatGPT for medical summaries, responding to patient questions, documentation, or notes.
AI in the healthcare space will grow to a $187 billion market by 2030, these facts illustrate that AI will have an important part of the future healthcare system.
FAQs
Q1. How is AI currently used in hospitals?
AI is being used in hospitals for diagnostic imaging, virtual nurses and assistants, electronic health record management, and to predict patient deterioration.
Q2. Can AI replace healthcare professionals?
AI can complement healthcare professionals, but it will not replace them as AI can take care of tedious tasks to allow doctors to focus on critical thinking and patient care.
Wrapping up:
AI technology is quickly changing the fabric of healthcare – allowing us to achieve speedier and more accurate diagnoses using data.
It facilitates fully personalized treatment plans based on a patient’s specific genetic and medical history, and guaranteeing continuous health monitoring well after treatment using smart devices and virtual assistants.
From early detection to long-term care, AI is offering exciting new possibilities to enhance patient outcomes, reduce healthcare costs, and make medical services more proactive and accessible.
Now, build your healthcare AI skills with Big Data Trunk
Learn how to explore, clean and model highly complicated healthcare data using Python libraries including Pandas, NumPy and Scikit learn. Use AI to enhance diagnostics, treatments and patient care.
.
