How can AI detect diseases earlier?

Analytics and Artificial Intelligence (AI) is converging and it is imperative that we rise above the current Generative AI / LLM hype and treat AI as a portfolio of tools and capabilities (machine learning, NLG/ NLP, deep learning/neural networks, computer vision, generative AI, medical robotics) which can be brought to bear to craft a solution to a problem that best delivers value, versus being enamored with newer technologies like Generative AI that need to mature and be rendered free of "hallucinations" for this to be impactful in a clinical context.

A great example that I have seen with generative AI is on the preventative side of health. Helping plan out lifestyle changes; coaching plans and meal plans can be easily achieved and with the conversational side of Generative AI these plans can be adjusted quickly with user feedback.

AI has several applications and can be also used along with computer vision to improve the capabilities of the application. There are many aspects that AI can be integrated even with a low implication of human validations.

As an active participant in numerous AI initiatives within the medical and healthcare field this year, I have witnessed firsthand the profound impact that AI and machine learning are having on disease detection. The implementation of these technologies has become increasingly evident and is poised to revolutionize the way diseases are identified. By alleviating physicians from administrative and paperwork burdens, AI tools enable them to maintain a patient-centric approach, resulting in improved accuracy of diagnoses and reduced physician burnout. This symbiotic relationship between AI and healthcare professionals ultimately benefits both patients and doctors, fostering a more efficient and effective healthcare ecosystem.

- A very practical example is pasting your bloodwork results (without your name and insurance number) in ChatGPT4. AI can analyze and even give you tips what to be aware of and what you should keep an eye on.

Artificial intelligence has the potential to delve deeper into data through advanced deep learning, a subset of machine learning. This can uncover complex patterns and hidden details in large data sets, revealing early signs of disease. For example, a deep learning algorithm can sift through thousands of chest scans to find indicators of pneumonia that might go unnoticed by the human eye.

So far AI had a great results when diagnosing diseases using image recognition and computer vision techniques. AI has outperformed doctors when it comes to reading scans to identify diseases such as cancer. But it also has shown good results when trying to predict the likelihood of getting a disease such as having a stroke.

AI can detect diseases earlier through medical imaging analysis, predictive analytics, wearable devices, remote monitoring and early disease risk assessment. AI algorithms can analyze medical images for abnormalities, predict disease risk based on patient data, monitor vital signs continuously, extract insights from vast unstructured data, assess individual risk profiles, and expedite drug development, leading to proactive interventions and improved healthcare outcomes.

It is often helpful to experiment with putting different types of medical documents into chatgpt4 with the code interpreter enabled. When you have multiple sources the AI is able to give more information and better predictions.

Recent advancements in AI make it possible to monitor patients with risk of Alzheimer's, Parkinson's and Dementia thru wireless monitoring coupled with Artificial Intelligence (Machine Learning, Deep Learning, Natural Language Processing and generative AI) from an analysis of their movement, gait balance etc. and treat them early. In Asia, Medical Robots are also being used to scan shoppers in Retail / Department stores for fever while interactive voice robots are used to comfort seniors and help them with their daily tasks, as well as measure vital signs like blood pressure, sugar etc.

AI can provide critical support in mental health care, helping to detect subtle changes in a person's speech, writing, or online behavior that may indicate early signs of conditions such as depression or anxiety. At a time when mental health is increasingly recognized as a critical aspect of overall health, AI provides an additional tool in the diagnostic arsenal.

Using data, we can predict the upcoming real differences from Human Organs.. To get better benefits we also need to provide the suggestions or. Methods to avoid or reduce the alerts by mapping doctor general advice against the categories of alerts will help every one.. Also. Some specific cases suggesting specialist in same locality using MAP.. Over the extend we can get their consent to book auto appointment..

AI can identify subtle signs and patterns indicative of disease at an early stage, often before symptoms manifest. This allows for timely intervention and treatment initiation, leading to better health outcomes. Early detection and intervention can prevent diseases from progressing to advanced stages, where treatment is often more complex and costly. By catching diseases early, AI can help reduce healthcare expenditures associated with long-term management and complications. Overall, AI for early disease detection holds the potential to transform healthcare by saving lives, improving outcomes, and optimizing resource utilization.

Another challenge worth mentioning is the reproducibility issue. There are several AI health applications that excel in labs but fail miserably at clinical trials. Others challenges include the legal implications of AI misdiagnosis. Who is responsible if a patientis misdiagnosed, is it AI? The doctor? The companythat developed and trained the AI system? Or the hospital who adopted AI? ...the responsibility and accountability lines are blurred.

It’s worth remembering that while math and statistics are inherently unbiased, we need to be wary of bias in results produced by AI. Whether it’s through the labelling of data, the order of operations of the algorithm, or the interpretation of results, AI is created by biased humans.

When it comes to AI based diagnosis and investigation, we tend to become the masters of the tool but almost never question the level of domain expertise we have (in healthcare). If a disease has been detected and diagnosed by humans and they use N factors to deduce it, it is almost always possible that there are few additional parameters that lead the diagnosis in a particular direction. For AI to diagnose a disease that is rare or new, is almost impossible without multiple Expert Systems being networked together with continuous deep learning modules being run with an evolving knowledge base. It can get better only with time and we will still rely on growth in human intelligence to assist complex AI models, imho.

AI could be really accurate for most of the diagnosis, for diseases it is already trained on, but if a new and previously unknown disease appears (which if you see biological history till now happens all the time) it may confidently misdiagnose it. Therefore, it won't be responsible to completely give the entire authority to AI, rather it would be much better if it acts as assistant to real doctors, at this point. That being said one of the key factor here is transperancy of results, which can be achieved using AI explainability. If AI can help doctors interpret why it thinks given results are true, doctors will have a better judgement.

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (Kumar et al., 2021) Evaluation of artificial intelligence techniques in disease diagnosis and prediction (Vasal et al., 2022) The challenges of artificial intelligence for early disease detection (Gupta et al., 2022) These papers provide a good overview of the challenges of AI for early disease detection, and they discuss some of the potential solutions to these challenges. I hope this helps!

You can participate in AI hackathons and competitions, often with a healthcare theme, where you can work on real-world problems and learn from experienced mentors. These events are excellent opportunities to innovate, network, and turn your ideas into marketable products. If you're a student or researcher, consider writing a thesis or conducting a study on AI in healthcare. This could include developing new algorithms, exploring ethical considerations, or assessing the impact of AI on patient care and outcomes. Getting involved in advocacy is another powerful way to contribute.

Diagnosis and early disease detection can be made with medial imaging combined with genomics - expensive and regulated datapoints. The more important question is: can AI diagnose your health with image/video from your smartphone camera combined with other smart devices?

Generative AI is not ready for diagnosis today! Machine Learning, Deep Learning, NLG, NLP and Medical Robotics need to be creatively leveraged as a portfolio for diagnosis as well as innovative applications like patient engagement, robotic surgery, data capture from Patients, multi-lingual communication, comforting and helping seniors etc.

AI algorithms may exhibit biases due to imbalanced training data or underlying assumptions. Healthcare providers must mitigate bias and ensure fairness in AI-driven decision-making, especially regarding sensitive issues such as disease diagnosis and treatment allocation. Also, Healthcare providers must prioritize data privacy and security when collecting, storing, and sharing patient data for AI-driven disease detection. Implementing robust cybersecurity measures and adhering to regulatory requirements such as HIPAA (in the United States) are essential to safeguard patient information.

Data required for AI to predict diseases: For AI to predict diseases, healthcare data is essential which cannot be directly used due to its sensitivity, regulatory and legality norm in most of the countries. Artificially created non reversible 'Synthetic data' which will mimic both the discreet and non-discrete variables and have the inter variability correlation of the actual healthcare datasets, will play the vital role. This data can be collected from healthcare data aggregators and can be used to not only train AI models to predict diseases but also for the following- 1. In silico clinical trials 2. Research feasibility 3. Population health and well Being 4. Digital patient Twins 5. Training Datasets (AI/ML)