search.noResults

search.searching

saml.title
dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
Regulatory


Safe changes To foresee future patient risks, the FDA is proposing a framework whereby manufacturers identify and describe which aspects of their devices will change as they learn, and how their algorithms will remain safe and effective throughout. As originally laid out in the 2019 discussion paper, this ‘Predetermined Change Control Plan’ consists of “SaMD Pre-Specifications” identifying the aspects of a device that will be modified through machine learning and an “Algorithm Change Protocol”, which explains the methodology for implementing those changes without compromising safety or effectiveness. Even with this information from a manufacturer, it can still be difficult for a regulator to predict a device’s true risk over the course of its life cycle.


Continuous learning devices can be extremely sensitive to changes in training data, which can cause a big change in performance, explains Johan Ordish, group manager for medical device software and digital health at the MHRA in the UK. That creates extra challenges for regulators trying to classify a device, which the MHRA does based on its risk profile.


In its 2019 discussion paper, the FDA acknowledges that it may not be possible to predict how a device will change. Its proposed framework is intended as a process for using available evidence to identify as much future risk as possible at a pre- market stage. In other words, it’s a way to make the best of what’s available.


While the MHRA currently has requirements aimed at managing a device’s risk across its life cycle – placing the responsibility on the manufacturer to ensure that the performance of their model is maintained over time – frameworks such as the FDA’s “predetermined change control plan” could help with the challenge of anticipating risk change in continuous learning devices, says Ordish. Development of the framework is likely to be iterative and the FDA plans to issue draft guidance that will be open to public comment later in 2021.


Patients and practitioners Manufacturers will also be required to provide information to users about the risks, benefits and limitations of their devices, per the FDA action plan’s commitment to building patient trust and transparency. However, a truly-patient centric approach must involve healthcare professionals. It is clinicians who obtain patient consent, which could be the deciding factor in whether a device is used at all. Clinicians are not mentioned within the plan. The FDA does not regulate the practice of medicine nor the conduct of clinicians, but can


Medical Device Developments / www.nsmedicaldevices.com


create obligations that ensure they are as supported as possible when using AI medical devices. “What the FDA can do is mandate some requirements and put [it] on the manufacturer, for example, to offer some educational programmes to physicians,” says Harvard research fellow in medicine, artificial intelligence, and law, Sara Gerke, whose research on regulating AI is referenced in the FDA’s action plan. Clinicians could be trained to use AI in the same way they’re trained to give medication, says Dr Indra Joshi, director of AI for NHSX. While they may not know the ins and outs of its chemical properties, clinicians know what medication does and what to do if something goes wrong. “If you just take that same ethos and apply it to AI, [you can] say: ‘This is what you need to look out for and this is what you need to do’,” she explains. Communicating this could be as simple for manufacturers as putting in place a service-level agreement with the clinicians that prescribe and use their devices.


Changes in the data used to train continuous learning devices can greatly impact their real-world performance.


“What the FDA can do is mandate some requirements and put [it] on the manufacturer, for example, to offer some educational programmes to physicians.”


Sara Gerke, Harvard research fellow


Even so, manufacturers should be mindful that increased user transparency doesn’t come at the cost of usability or efficacy, notes Timo Minssen, director of the Centre for Advanced Studies in Biomedical Innovation Law at the University of Copenhagen. For example, a device that detailed each of its calculation steps and


29


Phonlamai Photo/Shutterstock.com


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133