Artificial intelligence (AI) has been promising that every sector will become permanently transformed.
The medical industry has been using it for many years, and it continues to hint at how the future of treatment will be easier and more accurate. AI in medical diagnostics could be the backbone of that. How well can AI spot disease, and is it more competent than humans at some clinical tasks?
Where AI in Medical Diagnostics Excels
Hospitals, doctors and researchers are using AI in cases where it is known for accuracy. Modern research shows it has delivered results that would take doctors a year to generate in a couple of weeks. It makes many processes faster — some without the need for human intervention. What are some of the tasks AI can empower?
Personalised Medicine
It is nearly impossible for doctors to consider every patient's genetic, lifestyle and medical factors. This type of understanding would require hours of dedicated interviews and study on one individual, which is impractical given how many people need to be seen.
An AI in medical diagnostics could scan records to develop a more customised care plan that considers all the personal details. It would find the perfect medicine for the best chance at a response, boosting the likelihood of success.
Image Analysis
Machine and deep learning can train on countless images from medical devices, including X-ray and MRI machines. It could even pull from CT scans and pathology slides. However, many companies are still underutilising these resources, with digital pathology adoption only at 5%-10% — it will hopefully be 90% in the coming years.
Over time, cameras will become better at identifying trends and anomalies in pictures, noticing what the human eye misses. With the ability to zoom and clarify subtle details, image analysis is one of the best ways to find early concerns and diagnose more precisely.
High-Throughput Screening
Triage is a hurdle ruining hospitals' reputations and delaying patient recovery. AI can review high quantities of data faster than manual screening, giving visitors faster answers on the tests that matter most. Consider how AI could pass over many mammogram scans within seconds instead of days with a radiologist, giving people near-instantaneous answers. However, researchers suspect it depends on the doctor how well AI would perform in these circumstances.
Early Detection and Prediction
AI will detect patterns as algorithms continue learning about patients. It may even notice risk factors on a local level, pointing out adverse regional health impacts, like pollutants leading to specific diseases. AI determinations have become more capable of discovering diseases and predicting early warning signs.
Reducing Diagnostic Errors
Human error is a common deterrent in the medical field. Sometimes, a doctor misses a critical detail or an image confuses them into thinking a patient has some other ailment. AI could corroborate diagnoses, highlighting concerns and possible errors that could arise.
Automation and Efficiency
Doctors have to fill out, organise and send a lot of paperwork. Data entry is another laborious and time-consuming task that takes away from one-on-one patient attention. AI excels at automating repetitive tasks so medical experts can focus their energy on higher-value tasks, like building patient relationships and working on tasks where AI may not be as effective.
Where AI Has Limitations
AI can support professionals with many aspects of their work, but where will the human touch still be necessary when detecting illness?
Lack of Context and Empathy
While AI can talk like a person, it can’t understand the emotional context of a medical case. In a more nuanced scenario, an algorithm may not be able to determine how less-concrete metrics, such as morals and values, can influence how someone receives care.
Additionally, while AI could accurately deliver or predict a disease, it would also not know how to convey this information to patients with the compassion a human could.
Transparency
Traceability is one of the most significant issues in AI right now. It has led to several lawsuits in which some algorithms have used stolen content for training. Medical experts must be cautious, as personally identifiable patient information must be used ethically and compliantly.
Explainable AI (XAI) is becoming more normalised, which will point out where algorithms derive determinations from their dataset. XAI must become nonnegotiable in the medical sector to prove how well it can detect disease. This will help address other regulatory and ethical concerns that arise from using AI in medical applications.
Bias
AI bias comes in many forms, whether due to data gaps or deliberate training choices. Many datasets become biased because there are underrepresented demographics, which could occur due to limited research efforts. This is a call to action for all doctors to invest in mending these biases. Otherwise, AI could say something inaccurate or hallucinate a determination.
Deskilling
Doctors who become too reliant on AI to discover diseases for them could get complacent. They could eventually forget the finer details of their education, self-sabotaging their own diagnostic abilities. This is avoidable if they use AI as a learning tool and internalise what it could teach them.
Implementation Costs
Incorporating more advanced AI algorithms for disease detection is limited to the hospitals that can afford it. Rural facilities would not be able to install them without governmental assistance, which requires advocacy and legislative action. Additionally, it requires more than incorporating one algorithm.
Experts must also purchase additional infrastructure, like data storage, software and training tools. In addition to these features, onboarding new professionals, such as data scientists or AI engineers, may be necessary to upskill doctors and maintain quality. Fortunately, the expense is justifiable, given the economic benefits AI would bring.
Can AI Spot Disease?
Researchers have proven that AI is a powerful tool for finding illness and complementing human efforts. However, it still needs refining to consider the more emotional aspects of medical care. Fortunately, as a practical tool, it can free up doctors’ time and energy to dedicate to these pursuits while delivering expedited and accurate diagnoses with the help of technology.