Overview

Medical diagnostics evaluates health conditions and diseases by analysing symptoms, medical history and test results.¹

Diagnostic laboratories are emerging that will make intelligent decisions with minimal human supervision. Advancements in technology, early disease detection, reduction in time required to receive treatment and short working time will create more opportunities in the health industry.

Evolution of diagnostics

In Egypt and Mesopotamia, wealthy people were treated according to their symptoms; observing the skin, listening to the heart and lungs, and examining the urine by dropping it on the floor and tasting it. The lower classes relied on divination through ritual sacrifices. 

During the Middle Ages, diseases were divine punishments. Many inventions and discoveries (the invention of the microscope and the discovery of blood circulation) in the seventeenth and eighteenth centuries laid down the principles of diagnostics. 

The nineteenth century saw refinements in diagnostic tools: stethoscope, ophthalmoscope, and laryngoscope, as did microscopes and X-rays. Society transformed from religion and aristocracy to industrial, commercial, and professional. Emerging technologies reduced dependence on patients but increased dependence on capital equipment and organised medical practice. 

Antibiotics were discovered, and medicines were rolled out in the market. Then came the twenty-first century which brought artificial intelligence. We have witnessed disruptive technologies make widespread disruptions. Healthcare is no exception 3D printing, robotics, nanotechnology and AI are digital technologies shaping the future. 

Artificial intelligence in diagnostics

AI is the science and engineering of making intelligent machines through algorithms or rules so that they can mimic cognitive functions like humans and learn problem-solving.³

AI is an umbrella term

Machine learning (ML) allows a machine to do anything that requires human intelligence. It enables machines to learn by analysing training data and improving their functions over time. ML algorithms requiring datasets for training are supervised while the unsupervised algorithms do not rely on data to learn.

Deep learning is a subset of ML. It is composed of interconnected layers, like neurons connected to each other. They receive input from outside, and the information travels through layers, the final output layer predicts or classifies the object in the image.

Natural language processing (NLP) is the process of enabling machines to understand means of human communication. Medical records thus can be processed rapidly and incorporated into ML. The ability of machines to “understand” human language and subsequently analyse this data is the key to integrating human language into predictive models.

Computer vision is the branch of artificial intelligence that interprets texts, images, and videos. Computer vision through integration with ML neural networks has found other applications. They include operative note generation and forewarnings regarding the risk stages of the operation.

Benefits of AI in diagnostics

Improved accuracy and precision

Cancer diagnosis and its early detection is by image analysis. AI algorithms have made medical imaging accurate and an invaluable tool for cancer detection. They detect medical scans and identify conditions that human eyes miss.

The advantage of using diverse data to conclude is that it leaves no room for errors. Clinicians can understand patient health and the cause of their symptoms.¹

Increased efficiency

Automation of routine procedures frees healthcare professionals to focus on more complex issues.

Improved accessibility

 AI-powered telemedicine and remote diagnostic tools extend services to remote areas and ensure quality healthcare.⁵

How AI works in medical diagnostics

• The use of computers in medical care has generated large amounts of data. These datasets are used to train machines⁸
• Medical diagnostics aims to provide effective treatment through accurate diagnosis using X-rays, MRI, CT scans, blood tests and biopsy procedures. With more data to train prediction, accuracy, speed and efficiency of diagnosing improves
• Images (from X-rays, ultrasounds, PET, CT, MRI scans), bio-signals (from ECG, EEG), vital signs (pulse rate, blood pressure, respiration, body temperature), medical history, demographics, and laboratory tests are analysed by the algorithms. The interpretation supports decision making by clinicians⁹

Uses of AI

• In radiology, AI systems analyse X-rays, CT scans, MRI, and PET scans to detect tumours
• In pathology, there is automated analysis of tissues at microscopic levels to identify pathologic tissues
• Genomics enables tailored treatment plans by analysing genetic data and in the early detection of genetic disorders
• Early diagnosis of neurodegenerative diseases like Alzheimer's helps to delay the progression of the disease⁴

Diabetes is detected early, and this in return helps to prevent blindness. Cerebral stroke and chronic obstructive pulmonary disease when detected early prevent morbidity.

• In wearable technologies, data generated from APPLE watches, Fitbits and several other wearable devices provide continuous real-time monitoring, allowing precise treatment adjustments and detecting abnormalities

Challenges in using AI

Data privacy and security

To maintain the confidentiality of patient data and ensure security measures.

Bias

Since the data is mostly from developed countries, it has a bias.

Integrating AI with existing systems

It requires investment and training of healthcare professionals.

Artificial Intelligence: future of medical diagnostics 

Increased life expectancy and emergence of new technologies will impact healthcare. AI will not only impact diagnostic capabilities but will also assist in other ways. 

Predictive care 

The healthcare system will be able to anticipate when a person can develop a chronic disease. AI powered systems will suggest preventive measures before worsening of their symptoms.

Personalised medicine

It will help in creating tailored treatment plans for patients individually.⁹ It improves outcomes and allows efficient use of medical resources. AI systems help to diagnose diabetic retinopathy and prevent blindness in many.

Virtual healthcare assistants

They will provide advice on treatment and medications. They will make the patient more compliant by advising them to take treatment or do exercises.

Telemedicine and virtual hospitals

Telemedicine and wearable technology will make monitoring of patients easier remotely. This will help in holistic patient care. Virtual hospital wards with central locations will monitor many patients in their homes.²

Virtual and augmented reality

Is helpful in managing chronic pain. It is more effective than the traditional pain management and has no side effects.⁶

It helps the medical professionals to have a learning environment; practise their surgeries, and hone their diagnostic skills.

Elderly care

With increased life expectancy of old people is increasing, virtual hospitals and healthcare assistants will provide them better support.

3D printing

The viability of organ printing is under research. If possible, this will help to find a solution for organ shortages.⁷

CRISPR gene editing

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a gene editing technology. It influences the treatment of many diseases. It cuts out the infected DNA strands to prevent diseases like cancer and HIV. Integrating AI with CRISPR increases the accuracy of biological engineering.

Drug discovery and vaccine development

AI models excel in predicting effective antigens and optimising formulations, as seen in the rapid development of the COVID-19 vaccine.

AI and medical professionals

Despite automation and digitisation in medical diagnostics, doctors are irreplaceable. The reason for this is

• Machines cannot show empathy
• Machines lack creativity and problem-solving skills
• Competent professionals can handle complex digital technologies
• AI will take over the repetitive tasks of analysing scans and sifting records, saving time
• AI will spot anomalies and correlations humans cannot make, like detecting diabetes through voice recognition

Skills required by medical professionals

Medical professionals will need to learn to communicate with algorithms. Medical professionals will need to master the art of writing prompts to use AI to its full potential. Those who do that will be effective in human-AI partnerships.

FAQ

Will AI change medical practice?

AI has transformed healthcare. Together, they have changed the scenario in medical practice with accurate diagnosis, treatment planning and improved patient outcomes.

What is the future of medical imaging?

Medical imaging is evolving with technological advancements. There is an ongoing search for new algorithms and methodologies to improve medical imaging.

How does AI improve medical diagnostics? 

AI uses algorithms or sets of rules to analyse vast data banks to identify patterns and relationships to improve medical diagnostics.

Summary

AI includes weak and strong intelligence. Strong AI or General AI is designed to think, understand, learn and create new knowledge and work like the human brain.⁷

AI in healthcare marks the advent of a new era. It impacts every technique used in diagnostics by increasing accuracy, reducing variability, and streamlining workflow.

As AI advances and integrates into diagnostics, it improves patient outcomes. Staff shortage, human errors, long working hours, and pricing have continued to bog down the healthcare industry. With AI casting its influence on decision-making and services, healthcare is in for leaps.