Synopsis
A clear-eyed look at where AI is genuinely helping patients and clinicians, and where it is quietly failing them, across diagnosis, drug discovery, fraud, bias, privacy, jobs and regulation.
Read Chapter 1 free, no account needed: Chapter 1 of AI in Healthcare.
What you will get from this book
- An understanding of how AI-enabled medical devices grew from six to about 1,500, and what the evidence does and does not prove.
- A look at how AI is used in radiology, drug discovery, hospital operations, claims and fraud detection.
- The reasons diagnostic AI fails on atypical cases and how algorithmic bias arises.
- Knowledge of healthcare data breaches, staffing and training, and why regulation lags.
- Practical guidance for clinicians, patients, administrators, policymakers and support staff.
Who should read it?
Adult readers: clinicians, healthcare administrators, billing and compliance staff, students of health and technology, and patients who want to understand the tools now used in their care.
Quick answers: what this book covers
- Topic: AI in healthcare, evidence, risk and governance.
- Audience: adults, 18 and up.
- Length: preface, 13 chapters and an afterword.
- Cost: free to read on Qalam and Kitab.
Frequently asked questions
Does AI in healthcare actually improve patient outcomes?
In some narrow tasks, yes. But the book cites an analysis finding that only 3 of 1,357 FDA-cleared AI devices had been evaluated against patient-centred outcomes.
Why does diagnostic AI fail?
It performs best on typical cases and fails on atypical ones, such as a pneumonia patient with no fever, which is why clinician judgment must stay in charge.
What is the 510(k) pathway?
About 97 percent of cleared AI devices reached the market by showing they are substantially equivalent to an existing device, which is not the same as proving patient benefit.
Should I trust a chatbot’s symptom advice?
No. If something feels wrong, see a doctor. The book calls skipping care based on a chatbot the most dangerous number in it.
This is educational information, not medical advice. The book’s scenarios are illustrative composites. Researched and drafted with AI assistance and reviewed by Qalam & Kitab for accuracy before publication.
Table of Contents
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1.
Preface: The Question Nobody Asks First
Most conversations about artificial intelligence in healthcare start in the wrong place. They start with the technology, what it can do, how fast it is spreading, how much money is…
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2.
Chapter 1: The Wrong Kind of Wrong
Every industry says it is being transformed by artificial intelligence. Healthcare actually has a claim to that word that most industries do not, because the cost of getting it wrong…
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3.
Chapter 2: Six Devices to Fifteen Hundred
In 2015, the FDA had authorized six AI-enabled medical devices. Most clinicians had never encountered one. Most patients had never heard of one. AI in medicine was a research topic,…
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4.
Chapter 3: The Machine That Reads Your X-Ray
If you want to understand what healthcare AI looks like when it is working, picture a radiology reading room, described here as a representative example of how these tools are…
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5.
Chapter 4: The Drug That Wasn't There Yesterday
If diagnostic AI is about reading existing data faster, drug discovery AI is about generating entirely new candidates, and the pace of investment here has been extraordinary, along with the…
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6.
Chapter 5: The Hospital That Runs Itself
The AI that gets the headlines reads mammograms and predicts sepsis. The AI that actually keeps hospitals running does something far less dramatic: it forecasts how many beds will be…
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7.
Chapter 6: Follow the Claim
Before going further into money, fraud, and risk, it helps to understand how healthcare actually gets paid for, since AI touches every step of this chain differently, and the financial…
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8.
Chapter 7: The Fraud We All Agree On
Healthcare fraud is not a minor leak in the system. The National Health Care Anti-Fraud Association's conservative estimate puts fraudulent claims at roughly 3 percent of total US healthcare spending…
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9.
Chapter 8: The Patient Who Didn't Have a Fever
ECRI, the patient safety organization formerly known as the Emergency Care Research Institute, named “navigating the AI diagnostic dilemma” the single top patient safety concern in all of US healthcare…
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10.
Chapter 9: The Algorithm That Learned Our Worst Habits
The best-documented case of bias in a healthcare algorithm is not hypothetical or recent. It was published in the journal Science in 2019, and it should be required reading for…
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11.
Chapter 10: Two Breaches a Day
Healthcare data breaches are not a rare event. They are a near-daily one. According to HIPAA Journal's analysis of federal breach reports, 772 large healthcare data breaches, each affecting 500…
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12.
Chapter 11: The Nurse Who Saved an Hour
Healthcare's staffing crisis is real and well documented independent of AI entirely. The Association of American Medical Colleges projects a shortfall of up to 124,000 physicians in the United States…
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13.
Chapter 12: The Rules That Aren't There Yet
Given everything in the previous chapters, healthcare AI's regulatory oversight is thinner than most readers would assume. Roughly 97 percent of FDA-cleared AI medical devices reach the market through a…
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14.
Chapter 13: What to Do on Monday Morning
Pulling every chapter together, a few things are true at once, and none of them cancel the others out. AI has produced real, measurable clinical benefits in specific, well-defined tasks:…
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15.
Afterword: What Actually Changes Things
Return to the sepsis scenario from the preface, and imagine it a year later. The alert system has been updated twice. The false alarm rate has come down. The nurses…
Read Chapter 1 free, no account needed: Chapter 1 of AI in Healthcare.
What you will get from this book
- An understanding of how AI-enabled medical devices grew from six to about 1,500, and what the evidence does and does not prove.
- A look at how AI is used in radiology, drug discovery, hospital operations, claims and fraud detection.
- The reasons diagnostic AI fails on atypical cases and how algorithmic bias arises.
- Knowledge of healthcare data breaches, staffing and training, and why regulation lags.
- Practical guidance for clinicians, patients, administrators, policymakers and support staff.
Who should read it?
Adult readers: clinicians, healthcare administrators, billing and compliance staff, students of health and technology, and patients who want to understand the tools now used in their care.
Quick answers: what this book covers
- Topic: AI in healthcare, evidence, risk and governance.
- Audience: adults, 18 and up.
- Length: preface, 13 chapters and an afterword.
- Cost: free to read on Qalam and Kitab.
Frequently asked questions
Does AI in healthcare actually improve patient outcomes?
In some narrow tasks, yes. But the book cites an analysis finding that only 3 of 1,357 FDA-cleared AI devices had been evaluated against patient-centred outcomes.
Why does diagnostic AI fail?
It performs best on typical cases and fails on atypical ones, such as a pneumonia patient with no fever, which is why clinician judgment must stay in charge.
What is the 510(k) pathway?
About 97 percent of cleared AI devices reached the market by showing they are substantially equivalent to an existing device, which is not the same as proving patient benefit.
Should I trust a chatbot’s symptom advice?
No. If something feels wrong, see a doctor. The book calls skipping care based on a chatbot the most dangerous number in it.
This is educational information, not medical advice. The book’s scenarios are illustrative composites. Researched and drafted with AI assistance and reviewed by Qalam & Kitab for accuracy before publication.