Practitioners are no doubt aware of AI’s growing impact on health. A variety of AI health applications already affect individual health choices outside of healthcare environments. With smartwatches that can track reproductive cycles, metabolic rates and sleep patterns. Advanced technologies are now being deployed within healthcare environments to assist practitioners in collecting data, delivering timely advice and monitoring patient status.
Digital medical platforms powered by AI and deep learning are also being used to enhance clinical accuracy, develop treatment plans and improve patient outcomes while collecting high-utility data. With the help of billions of data points, these programs will assist clinicians in improving patient outcomes through a variety of digitized medical services. AI for medical diagnosis will incorporate population data, genetic data and behavioral data to identify risks to health and support accurate disease diagnosis. AI’s impact on healthcare is not just limited to the effective treatment of disease but will also change practitioners’ job descriptions as they learn to use it.
AI to Diagnose Disease
AI for medical diagnosis uses algorithms powered by huge volumes of health data to analyze symptoms, interpret lab results and analyze patient histories to identify diseases or disorders. These algorithms are trained using huge volumes of statistics, statistical models and deep learning programs to create disease diagnosis models for efficient and accurate disease diagnosis. Already, these algorithms are reducing human error and removing human biases from the clinical process. While human clinicians are capable of incredible feats of intellect and inference in the clinical arena, they also suffer from unconscious racial, gender and disability biases. Trained practitioners can also occasionally make false assumptions and misuse the clinical heuristics learned during their clinical education, overgeneralizing their own experience or misunderstanding clinical details.
Not only is AI making clinicians more accurate but also faster, with AI platforms able to provide rapid differential diagnoses for complex cases by comparing thousands of data points instantly. This process, called associative inference, compares the case-specific data points to existing cases to draw highly accurate correlations between patients. The most advanced models also incorporate causal analysis of disease origin and symptomology, effectively considering alternative explanations in real time.
AI-based diagnoses are particularly effective in cases of serious disease where the stakes are very high, the clinical presentation is complex (and sometimes contradictory) and pressure on clinicians to get it right the first time is immense.
Limitations of AI in Diagnose Disease
While AI applications can be effective, they are not without their limitations. Algorithmic and statistically based diagnoses are powerful, but they can be inaccurate. In one study, AI and human clinicians performed about the same, with AI accurately identifying disease 72.5% of the time to humans’ 71.5%. Of course, given the millions of clinical diagnoses made every day, small differences in overall percentages project into thousands of successfully treated patients. However, AI is still getting it wrong three out of every 10 times.
As a result, AI is unlikely to be responsible for clinical decision-making exclusively any time soon. Algorithmic models can be surprisingly rigid, unable to immediately adapt to changing variables. The digital platforms themselves still struggle to incorporate the important anecdotal, lifestyle and social data points that can be vital to diagnosing patients. Simple logistic hurdles like being able efficiently to upload and read hand-written physician notes can make clinical AI applications difficult to use.
Gaps in data and causal variables implicating disease can extend to what AI does well. The correlative models that account for AI’s usefulness can be incomplete, especially when little data exists. This could prove especially true for diagnosing pandemic diseases like COVID-19, where statistical trends supplant casual models due to the generalized symptomologies diseases like COVID-19 often present. AI can incorrectly assign a diagnosis based on assumptions it makes, failing to recognize changing infection patterns in populations as a clinician might.
How Practitioners will Use AI
Despite concerns about clinician unemployment, AI is not replacing human clinical decision-making any time soon. Clinicians will continue to play important roles in acquiring and applying patient data to the disease diagnosis process. They will certainly remain vital for the development and execution of treatment plans, especially in the modern healthcare environment where evidence-based, personalized care delivery is becoming the norm.
However, the deployment of clinical AI programs will lead to modification of the roles and responsibilities of healthcare providers, who are likely to focus more on collecting and interpreting data than they are on the actual disease diagnosis process. Across many industries, including healthcare, AI powers the output and visualization of important data but humans are still counted on to interpret what it means to a specific case. Accountability and transparency will still be key, as will excellent patient interview techniques necessary to supply AI platforms with the data they need to work.