AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. You'll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you'll learn how to handle missing data, a key real-world challenge.

Machine Learning (ML) has recently been demonstrated to rival expert-level human accuracy in prediction and detection tasks in a variety of domains, including medicine. Despite these impressive findings, however, a key barrier to the full realization of ML's potential in medical prognoses is technology acceptance. Recent efforts to produce explainable AI (XAI) have made progress in improving the interpretability of some ML models, but these efforts suffer from limitations intrinsic to their design: they work best at identifying why a system fails, but do poorly at explaining when and why a model's prediction is correct. We posit that the acceptability of ML predictions in expert domains is limited by two key factors: the machine's horizon of prediction that extends beyond human capability, and the inability for machine predictions to incorporate human intuition into their models. We propose the use of a novel ML architecture, Neural Ordinary Differential Equations (NODEs) to enhance human understanding and encourage acceptability. Our approach prioritizes human cognitive intuition at the center of the algorithm design, and offers a distribution of predictions rather than single outputs. We explain how this approach may significantly improve human-machine collaboration in prediction tasks in expert domains such as medical prognoses. We propose a model and demonstrate, by expanding a concrete example from the literature, how our model advances the vision of future hybrid Human-AI systems.

Artificial intelligence is now being successfully used to scan medical data and predict whether patients will have strokes or heart attacks. In a recent study, the AI system was more accurate at predicting these possible occurrences than doctors. The AI system works by learning from past medical record data and finding common factors between patients that have had heart attacks with people that might have heart attacks. As you might be able to imagine, correctly predicting sudden events likes strokes is a rather hard task that often results in doctors making very educated guesses. According to Futurism, Correct calls were made in 355 more cases than by doctors alone, which is a significant margin when you weigh that each correct case could mean the difference between life or death.