🧠 AI Could Save Millions of Lives—Here's How

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Welcome back AI prodigies!

In today’s Sunday Special:

• 🦾AI = Improved Patient Outcomes?

• 🦠The Radiologist Revolution

• 🔬Clinical Speed Readers

• 🧫Diagnostic Decisions, at Scale

• 🤖AI’s Impact (Sort of), Quantified

Read Time: 6 minutes

🎓Key Terms

• Machine Learning (ML): enables systems to learn and improve from experience without being explicitly programmed.

• Natural Language Processing (NLP): the ability of a computer program to understand human language as it’s spoken and written.

• Electronic Health Records (EHRs): an electronic version of a patient’s medical history that includes all the critical administrative and clinical data relevant to that patient’s care.

• Clinical Decision Support Systems (CDSSs): an application that analyzes data to help healthcare providers make decisions and improve patient care.

🦾AI = IMPROVED PATIENT OUTCOMES?

From patient diagnosis and treatment to administrative tasks, AI, via machine learning, natural language processing, and rule-based expert systems, will improve efficiency, accuracy, and patient outcomes.

🦠THE RADIOLOGIST REVOLUTION

Machine Learning (ML) algorithms train on large datasets of patient data to identify patterns and correlations that would be difficult or impossible for humans to detect. For example, ML algorithms are as accurate as human radiologists at detecting cancer in medical images. However, it’s unclear whether these results translate to a clinical setting, where scan quality, patient traits, and other variables are less predictable. Shortly, human radiologists will likely supplement AI-driven scans by administering and fine-tuning ML algorithms, interpreting their findings, and answering patient questions. Like most high-skilled jobs subject to automation, the absolute number of radiologists may decrease, the average radiologist’s skillset will improve, and unforeseeable jobs will emerge, just as in every other technological revolution.

🔬CLINICAL SPEED READERS

In addition to machine learning, other AI techniques, like Natural Language Processing (NLP), can equip healthcare practitioners with diagnostic superpowers. Through entity recognition, syntactic parsing, and semantic analysis, NLP can be used to analyze patient data such as Electronic Health Records (EHRs) and clinical notes. By categorizing each word or phrase (e.g., “cardiac arrest” → Medical Term, “D, B12, and Calcium” → Vitamins), entity recognition helps the model understand the substance. Syntactic parsing, on the other hand, classifies each word as a part of speech and identifies their relationships (e.g., “sudden” modifies “cardiac arrest”). Semantic analysis, an emerging sub-discipline, strives to uncover context, intent, and emotion. Unsurprisingly, most semantic findings are not immediately actionable, as the consequences of a recommendation can be dire. Together, these NLP techniques may help practitioners identify potential adverse reactions to treatment or limitations of a diagnosis. However, EHR data tends to be low-quality—riddled with seemingly (at least to the machine) arbitrary annotations and abbreviations—and unstructured—lacking columns and rows, with dramatically different context from patient to patient and appointment to appointment.

🧫DIAGNOSTIC DECISIONS, AT SCALE

In contrast to ML and NLP techniques, rule-based expert systems make recommendations whose underlying logic is transparent. Practitioners can trace “if-then” logic and other decision rules to the system’s conclusion. Clinical Decision Support Systems (CDSSs) help healthcare professionals diagnose diseases, prescribe treatments, and adjust recommendations. For example, a CDSS for diabetes management may include rules that recommend insulin dosage adjustments based on a patient’s blood glucose levels, dietary habits, and physical activity. The system might suggest increasing the insulin dose if a patient’s glucose levels are consistently high. Rule-based systems can also check for potential drug interactions to prevent adverse effects. For instance, a medication interaction expert system may flag a prescription if it detects that two prescribed drugs have known interactions that could lead to harmful side effects. It can provide alternative drug options or dosage adjustments based on a patient’s relevant characteristics such as age, gender, lab results, medical history, etc.

🤖AI’S IMPACT (SORT OF), QUANTIFIED

We wish we could tell you how many additional lives AI in healthcare may save. The field is evolving faster than academics and market research firms can administer studies. One study estimated that by 2023, an additional 22,000 cancer patients would survive each year thanks to ML-enabled early detection. With hundreds of other AI applications, thousands of other medical diagnoses, and millions of infirmed people worldwide, informed observers remain overwhelmingly optimistic about the future of healthcare.

📒FINAL NOTE

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