Today, AI is accomplishing feats in healthcare that would have seemed like magic just a decade ago. Recently, it has been instrumental in disease detection, aiding healthcare providers in early intervention and expediting primary care, screening, and treatment.
So, why focus on AI? Consider this: According to Cancer Research UK, the five-year survival rate for stage 1 bowel cancer exceeds 90%, but for stage 4, it plummets to 10%.
The crucial factor here is speed. AI can swiftly and accurately analyze health data, including symptoms, risk factors, and signs of illness, often surpassing the diagnostic abilities of human physicians and minimizing errors.
This, in turn, empowers healthcare providers to curtail expenses related to extended hospital stays, diminish the financial and emotional burden associated with late-stage conditions, and, most importantly, alleviate the suffering experienced by patients, thanks to early detection.
Be sure to read to the end to check the accuracy of these claims.
To begin, it helps to understand that AI encompasses a wide array of disciplines within mathematics and science. Essentially, any task a machine can perform automatically, often called “intelligence,” falls under the umbrella of AI.
One of the central subfields within AI is Machine Learning (ML), with Neural Networks and Deep Learning (DL) being its foundational components.
In the context of disease identification, AI draws upon a diverse range of subfields that have evolved from its core principles to provide valuable assistance.
Relationship between AI, ML, NN, and DL: Source
Remember this: Machine learning takes center stage. Furthermore, we’ll explore other domains that have sprung from it, such as Large Language Models (LLMs) and predictive analytics, which have demonstrated significant applications in healthcare.
AI excels at processing extensive and diverse data to yield precise outcomes. Deep Learning, for instance, improves the quality of CT scans by filtering out noise and preserving critical details, all while reducing radiation exposure.
AI and ML analyze a broad spectrum of medical data, encompassing bio-signals (ECG, EEG, EMG), vital signs, demographic information, medical history, and laboratory results.
This facilitates remote monitoring, advances early disease detection, and broadens data accessibility. Consequently, it enhances patient engagement and communication, cuts costs, and reallocates resources to more value-added tasks.
However, the accuracy of AI detection varies depending on the specific diseases and the quality of available data.
| DISEASE TYPE | AI/ML TECHNIQUES | ACCURACY (%) |
| Brain Diseases | CNNs, Genetic Algorithms, Random Forests | |
| Alzheimer’s | 92.98% | |
| Parkinson’s | 95.8% | |
| Breast Cancer | SVM, k-Nearest Neighbors, Decision Trees | |
| Breast Cancer | 99.51% | |
| Genetic Disorders | ANNs | 84.3 – 85.7% |
| Mental Illness | ML with Genetic Markers | 48 – 95% |
| Heart Diseases | SVM, ANN | |
| Arrhythmia | SVM | 89.1% |
| Arrhythmia | ANN | 85.8% |
| Cardiomyopathy | SVM | 80.2% |
| Cardiomyopathy | ANN | 85.6% |
| CHD (Coronary Heart Disease) | SVM | 83.1% |
| CHD (Coronary Heart Disease) | ANN | 72.7% |
| CAD (Coronary Artery Disease) | SVM | 71.2% |
| CAD (Coronary Artery Disease) | ANN | 69.6% |
The healthcare landscape continually evolves, with AI models emerging as potent tools for early illness detection and enhancing patient care by delivering timely and precise solutions. Let’s break down three noteworthy approaches that show significant promise.
Artificial neural networks, or ANNs, are the most powerful warriors of AI in illness detection.
Inspired by neurons in human brains, they are a network of highly interconnected processing elements (neurons) that mimic how biological nerves process information to solve problems. Too often, doctors have to deal with insufficient data, often with flaws.
ANNs offer a distinct advantage with adaptability, parallel processing prowess, and non-linear problem-solving abilities.
ANNs can learn on their own. Doctors don’t need to design complicated programs to solve problems; they only need examples, i.e., data. The working performance of an ANN is directly related to the training samples. If the training samples are incorrect, too few, or too similar, the working range and ability of the ANN can be reduced.
ANNs analyze patient data for more accurate disease diagnosis, considering symptoms, medical history, and test results.
ANNs can examine medical imaging data (e.g., mammograms, X-rays, MRI scans) to detect malignancies, predict cancer recurrence, and assess cancer risk based on genetics and lifestyle.
Researchers are using deep learning-based ANNs to improve early detection and diagnosis of gastric cancer, offering a more accurate and convenient alternative to traditional methods like endoscopy.
Using ANN models, Luk’s study achieves impressive sensitivity (96.97%) and specificity (87.88%) for liver cancer diagnosis. Additionally, CT image preprocessing combined with ANN improves early detection.
ANNs enhance sensitivity in colorectal cancer detection, using a combination of serum markers (CEA, CA199, CA242, CA211, CA724) identified through bioinformatics, offering a less invasive alternative to colonoscopy.
ANNs enable automatic medical transcription speech recognition (ASR), converting physician notes into text for efficient record-keeping.
Hospitals use ANNs to estimate patient hospital stays by analyzing their history, vital signs, and other data, improving resource allocation.
ANNs identify anomalies in radiological images, aiding in tumor detection, organ segmentation, and ECG data analysis for timely patient care.
While ANNs have generated a lot of excitement in healthcare, a few challenges and a lack of clarity still hinder their practical application.
Challenges like “black box” reasoning and data availability problems impede the full potential of ANNs in disease diagnosis and prediction.
Large Language Models or LLMs are deep learning algorithms that understand language patterns, including grammar, word order, and meanings, enabling them to comprehend and generate text effectively capable of grasping a whole language.
But how does this relate to disease detection and healthcare? Here, healthcare-specific LLMs come into play, which can be utilized in healthcare chatbots to understand and respond to patient queries.
Imagine a patient types in their symptoms, concerns, or health-related questions, and the LLM-powered healthcare chatbot or virtual assistance provides answers, offers essential medical advice, and even directs patients to the next most suitable courses of action (visiting a pharmacy or a doctor).
In mental health care, many AIs are designed to identify MH concerns, track moods, deliver cognitive behavioral therapy (CBT), and promote positive psychology. Well-known chatbots such as Wysa, Woebot, Replika, Youper, and Tess have taken charge of it.
LLMs streamline text annotation by mapping key terms to medical ontologies, enhancing healthcare documentation accuracy.
LLMs can recover missing patient data from unstructured text, reducing bias in patient outcome analysis. In one study, a model successfully recovered 31% of missing patient data, improving dataset fairness for patient outcomes.
LLMs identify and redact protected healthcare information (PHI) for effective privacy protection.
LLMs match trial criteria to patient attributes in electronic medical records, expanding the recruitable patient pool.
LLMs assist doctors in responding to patients’ basic health-related queries.
However, LLMs are not always great with medical jargon or specialized for domain-specific tasks. The language they speak is more of a general language.
They require special training to understand healthcare terminologies, categorize terms (e.g., “acetaminophen” as “Drug” and “cancer” as “Disease”), and handle synonyms (e.g., “acetaminophen” = “Tylenol” = “paracetamol”) by mapping tags to ontologies so the data can be searched or analyzed.
Training an LLM is a multifaceted and resource-intensive process. Whether you’re crafting a text-continuation LLM or a dialogue-optimized LLM, the following fundamental stages form the roadmap.
The initial and vital step is amassing a diverse text corpus. Data quality is essential for model performance. Then, data preprocessing includes tasks like removing HTML, correcting spelling errors, filtering toxic or biased content, converting emojis to text, or eliminating duplicates.
To train your model, you need to create input-output pairs. During pretraining, LLMs are trained to predict the next token in a sequence of text. Each word can be considered a token, or sub-word tokenization methods like Byte Pair Encoding can be used.
Now, you have to define the architecture of the language model. Many start with existing LLM architectures, like GPT-3, and adjust them as needed. Architecture tweaks, hyperparameter adjustments, and dataset customization are common ways to create a new LLM.
Tuning hyperparameters is a complex and time-consuming process. Using hyperparameters from existing research work can save time. It involves experimenting with parameters like batch size, learning rate schedulers, weight initialization, regularization techniques, and more to find the best setup for your specific model.
For dialogue-optimized LLMs, the process is similar to pretraining LLMs with an additional step called RLHF (Reinforcement Learning from Human Feedback). However, recent research by LIMA suggests that RLHF might not always be necessary, and you can achieve good results with high-quality data and supervised fine-tuning alone.
Pretraining and Supervised finetuning with RLHF
LLMs can be evaluated using intrinsic and extrinsic methods. Inherent methods assess their language modeling abilities, while extrinsic methods evaluate their performance in real-world tasks like problem-solving, reasoning, and competitive examinations.
A framework called the Language Model Evaluation Harness by EleutherAI and integrated by Hugging Face is used for this evaluation.
The best thing about LLMs is they’re always learning and improving — we can fine-tune available LLMs to do expanded specific tasks, making them even more precise.
Previous Advancements in Specialized Language Models for Healthcare:
In a nutshell, LLMs are opening new doors in healthcare, enhancing patient communication and care, and making healthcare more intelligent and responsive than ever in disease detection. This can streamline healthcare providers’ operations, helping them allocate resources more wisely and trim healthcare costs in the long run.
In the not-so-distant past, doctors predicted illnesses solely based on their experience and a patient’s history. This approach has limitations; after all, it all came down to the subjective judgment of a single provider and couldn’t account for all the variables at play.
Predictive analytics is changing this. It looks at historical data and predicts future health outcomes.
What’s impressive is that it doesn’t stop at the obvious stuff; it considers a whole range of factors, from a patient’s birthplace to their lifestyle choices, work habits, and even the local environmental conditions they’re exposed to.
All of these are thrown into the predictive mix to estimate the risk of chronic diseases
Take, for example, a study by Waljee et al. in 2017. They developed a predictive model based on electronic health record data that accurately foretold the risk of opioid overdose in patients with chronic pain.
Doctors and healthcare professionals can identify patients at a high risk of suffering from opioid overdose and take steps to prevent it before it even happens.
Leveraging AI – Predictive Analytics in Healthcare. Source
Predictive analytics (PA) in healthcare aggregates vast amounts of patient data incoming from EHR, insurance claims, administrative paperwork, medical imaging, etc., and processes it to search patterns.
With predictive analytics, healthcare providers can determine:
There’s more.
Healthcare professionals, organizations, and insurance companies use predictive analytics to assess the likelihood of patients developing medical conditions like cardiac problems, diabetes, stroke, or COPD, allowing for targeted interventions.
Predictive analytics help identify patients at risk of worsening conditions, such as diabetes patients developing renal disease or detecting early stages of sepsis, enabling life-saving early interventions.
PA can be used to analyze data on disease incidence, transmission rates, and other relevant factors to predict when and where an outbreak might occur. In a study by Zhang and the team in 2020, they developed a model to predict the spread of COVID-19 in China, which could accurately foresee new cases up to 10 days in advance.
Again, analytics can be employed to identify inpatients likely to exceed the average length of stay, helping providers adjust care protocols and avoid prolonged stays that can lead to increased costs and risks.
PA is also effective in predicting patient admissions and readmissions, enabling resource allocation, and improving patient outcomes. Kansagara’s study created a model with 70% sensitivity and 60% specificity to identify high-risk patients for readmission within 30 days of discharge.
Analytics can aid in efficient resource allocation, considering factors like patient utilization patterns, hospital capability, and expected demographic changes, ensuring resources are where and when needed.
Predictive analytics enhance patient engagement by identifying appointment no-shows, medication adherence, and effective healthcare messages, allowing for more targeted interactions and improved outcomes.
Emerging predictive analytics technology helps tailor treatments, especially for rapidly progressing diseases like certain cancers, by analyzing patient data to determine the most effective treatment regimens.
Predictive analytics can completely transform healthcare by forecasting disease outbreaks, at-risk populations, patient behavior, etc.
But, this is only possible once data intake is considered with utmost sincerity. The challenges like data quality and privacy must be overcome to their full potential.
Imagine tiny AI-powered nanobots or even ingestible capsules that roam through our bodies so they can catch signs of cancer or infection. That’s the future driven by AI.
The VR microscopy tools allow doctors to zoom inside tissues to observe disease traits virtually, thus making the invisible visible.
We’re seeing advancements like quantum AI (QAI) entering the research domain, which can speed up the development of diagnostic models.
The “Process for Progress in ALS” initiative developed MNd-5 in Canada. This AI program can detect early signs of ALS, often challenging to identify in its early stages (typically 21 months to detect).
MIT’s AI tool can now predict breast cancer five years earlier. UC San Francisco’s research on Alzheimer’s can predict Alzheimer’s six years in advance with over 90 percent accuracy.
ViviScout, a moonshot solution for identifying CVD diseases, uses AI and data to detect cardiovascular diseases, improving patient outcomes by connecting wearable devices to hospitals and ambulances and getting quicker help.
In the end, at its core, AI in healthcare is all about making better prevention. As we stand on the precipice of a new era in medicine, we’re not just talking about advancements in tech; we’re talking about advancements in the quality and length of human life.
The future of healthcare, guided by AI, holds the promise of earlier, more accurate diagnoses— ultimately, healthier and happier lives for all, no matter our location, background, or financial situation.