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Machine learning in healthcare: fundamental challenges vs. immense opportunities

Jun 06, 2022

9 minutes read

The latest developments in medical care mean a lot of pros to everybody, beginning with reduced unnecessary disability and increased life spans to better health equity and life quality. Besides all those benefits, the changing healthcare landscape means a heavy burden, especially for the finances — the USA is planning to spend no less than $6 trillion, or about $17K per person, on healthcare in 2027. 

Could AI and machine learning bring down costs and improve affordability, delivering healthcare advances simultaneously? We have looked into the benefits the healthcare facilities expect to reap while implementing specific machine learning use cases and have inspected the actual scope and state of using ML in medical care. In this article, you’ll read about the challenges organizations in healthcare face today.

A different story of using ML in healthcare

American medical care produces at least one trillion gigabytes of data each year. Given AI’s ability to self-learn large quantities of data, it is good news. Both executives and doctors hope to benefit from AI to test and discover new medicines more quickly, achieve better diagnostics, and make medical help more efficient and individualized. The most critical challenge is that healthcare is very different from other industries, making it more challenging to apply ML.

The first problem is is how we understand our diseases

Any disease consists of different processes that we might or might not understand. We risk pushing entirely incorrect or vague data to Ml algorithms, thus putting a patient’s well-being and health in danger. If the ML solution has been built on a false premise, it will cause more problems instead of providing advantages.

The second problem is is that we misunderstand the importance of safety

A human body can react and act in the most inexplicable ways, so we can’t expect even a well-tested system to perform correctly instead of leading to medical misdiagnosis and errors. This makes the whole paradigm training and evaluating of ML solutions (i.e., on a pre-defined and comparatively small dataset) to the problem. Extensive monitoring, cross-validation, and fine-tuning of medical care AI are necessary.

The third problem is how to work with data

Data processing and collection remain a problem despite the sheer amount of data generated in healthcare. Most of the data is not accessible for analytics and is siloed. For example, facilities gather unstructured data like performance reports and insurance claims, which must be converted, but they often miss insights-rich patient information. Medical data makes the analytics environment challenging.

Unlocking the potential of ML in medical care is difficult

  • The quality of data is often lacking, both in terms of scale and representativeness. The result can be a wrong conclusion (i.e., only respiratory tract infection can cause cough).
  • ML solutions function as a black box, which means that results may not be transparent and clear enough, making them hard to reproduce.
  • It is expected that ML solutions deliver the results by aligning with the clinical consensus, which blurs the line between erroneous findings and novel insights.

Even trained on a quality dataset ML solutions can be biased.
Those factors make building, designing, applying, and evaluating ML solutions challenging. Medical facilities need to implement changes in healthcare with full caution.

Benefits of ML-based solutions for medical care

All the limitations will not stop AI and machine learning from redefining healthcare. AI’s capacity to identify patterns in data that humans cannot realistically detect, and the self-learn possibilities are precious. 

Deep learning solutions can help doctors accurately detect anomalies (disease screening and diagnosis) and make more accurate decisions (advanced analytics plus automation). Other significant areas to take advantage of AI are FWA (i.e., fraud, waste, and abuse) and administrative efficiency. Let’s take a closer look at these.

01/ Enhanced medical diagnostics

Healthcare providers are moving from reactive to proactive behavior and more individualized medical care. Doctors need solutions that can quickly analyze large amounts of medical data and deliver insights about medical care improvements. ML solutions perfectly fit the goal.

Imagine that a doctor could see a patient’s heart failure or stroke risk based on closely examined lab test results, blood pressure readings, and socioeconomic factors like race, age, gender, etc. This can be analyzed and assessed by AI — to prescribe therapy or medication proactively.
Better diagnostics and treatment of patients mean lower costs of care, better outcomes, and increased satisfaction of patients.

Related: ML in medical imaging: feature extraction in diagnostics

02/ More precise disease diagnosis&screening

Medical imaging is the most future-oriented field for implementing AI in medical care. AI is getting more and more accurate in identifying diseases. The most important benefit of AI lies in precisely detecting anomalies in different medical images, beginning with simple camera photos (i.e., to detect skin cancer) and eye screens to MRIs, X-rays, SPECT, and PET images. All of this can be very significant in diagnosing lung cancer, esophageal cancer, diabetic retinopathy, and other conditions affecting the cardiovascular system, gastrointestinal tract, or eyes. 

Medical imaging solutions allow doctors to reduce treatment variability, diagnose faster, and improve patient outcomes and care processes. AI makes medical screening more accessible and affordable to lower-income patients.

03/Reduced waste, abuse, and fraud

ML solutions can help US healthcare save no less than $20 billion — cost savings realized by medical care providers and payers.

The are many cases of FWA, including drug diversion, falsification of medical records, unnecessary billing, illicit use of prescription drug benefits, and medical plan manipulation. Anti-fraud self-learning systems powered by machine learning can be prevented all these cases.

AI can identify specific patterns of behavior that providers, brokers, agents, and members exhibit when thinking about cheating the system. The patterns are updated on new data and enhanced to prevent FWA cases proactively.

Related: How Machine Learning reduces costs spent on treatment

04/  Better administrative efficacy

According to the statistics, the United States spends about 18% of its GDP on medical care. Despite those high costs, the US healthcare system is not consistently delivering affordable, convenient, and high-quality patient care. The most crucial problem is poor productivity.

The delivery of better health services is possible by implementing and adopting use case-specific ML-based solutions to either supplement or support roles and functions. For instance, entity and sentiment analysis solutions could help clinicians and administrative personnel improve outcomes by analyzing patient feedback. Some AI-based solutions could be helpful for readmission rate prediction to enhance patient recovery rates.


ML-based applications can improve administrative efficiency and healthcare delivery by:

  • Automating routine transactions done by non-clinical and clinical workers like managing fraudulent claims and regulatory documentation.
  • Streamlining and standardizing metric performance reporting between insurers and care provider systems.
  • Reducing administrative complexity through more flexible and fast interaction between payers and provider systems that process billing and insurance data.

In other words, ML-based solutions help to reduce bureaucracy.

The benefits of using AI in healthcare mean not only healthcare analytics, health prediction, and handling EHRs. AI can redefine healthcare by increasing its affordability. 

A combination of better decision-making (both clinically and business-wise), more accurate diagnosis, improved administrative efficiency, and reduced fraud and waste that AI brings to the table drives productivity improvements, which, as projected by McKinsey, will save from $280 to $550 billion by 2028. 

Hopefully, AI and ML will help the US healthcare system deliver more for less, radically changing its perception as the most inefficient medical system based on healthcare spending and outcomes.

Compared to RT-PCR, chest CT imaging may be a more reliable, practical, and rapid method to diagnose and assess COVID-19, especially in the epidemic area”, – the Radiology study authors wrote.

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