WEBVTT 1 00:00:00.000 --> 00:00:05.880 Good morning. My name is Trish Williams. I'm a Professor of Digital Health at Flinders University. 2 00:00:07.520 --> 00:00:13.280 Distinguished guests and friends and colleagues, we are gathered today on the traditional lands of the Kaurna people 3 00:00:13.280 --> 00:00:16.820 and we pay our respects to the elders past and present. 4 00:00:18.460 --> 00:00:21.040 Thank you very much for attending this presentation this morning. 5 00:00:21.120 --> 00:00:28.480 This is the second public lecture for Flinders University under the Brave Research and Innovation Series Programme 6 00:00:28.480 --> 00:00:35.460 and combined here today with the official programme for the very exciting 2018 Adelaide Festival of Ideas. 7 00:00:36.900 --> 00:00:42.800 Brave is not just a reflection of the extraordinary research that Flinders University pursues today, 8 00:00:43.060 --> 00:00:48.020 it's a call to action that links back to our earliest days as an institution. 9 00:00:49.280 --> 00:00:54.740 In 1966, Flinders University took its name from the intrepid Captain Matthew Flinders, 10 00:00:54.880 --> 00:00:56.820 the first person to circumnavigate Australia. 11 00:00:56.820 --> 00:01:04.120 And in doing so, it established a new university as a place where exploration and discovery would triumph. 12 00:01:05.080 --> 00:01:11.300 As we at Flinders make inroads into our mission and to change lives and to change the world, 13 00:01:11.520 --> 00:01:15.520 we're focused on making a difference by extending the boundaries of knowledge 14 00:01:15.520 --> 00:01:21.700 and by addressing the big challenges and by changing lives for better through our research. 15 00:01:22.400 --> 00:01:25.700 Our founding Vice-Chancellor, Professor Peter Carmel, 16 00:01:26.820 --> 00:01:29.400 coined his ambitions in the Flinders' now famous quote, 17 00:01:29.620 --> 00:01:33.280 We want to experiment and we want to experiment bravely. 18 00:01:33.980 --> 00:01:39.480 It's a concept that captured the imagination of successive generations of Flinders researchers 19 00:01:39.480 --> 00:01:41.920 and continues to inspire all of us today. 20 00:01:42.560 --> 00:01:48.760 This bravery, this idea of bravery in our researchers and our research discoveries 21 00:01:48.760 --> 00:01:51.800 are what we are doing to share with the world for the greater good. 22 00:01:52.720 --> 00:01:55.720 Such people as Professor Colin Raston, 23 00:01:55.800 --> 00:01:56.800 who pioneered green technology, 24 00:01:56.820 --> 00:01:58.020 green chemistry in Australia, 25 00:01:58.280 --> 00:02:01.760 and developed the ingenious Vortex fluid device, 26 00:02:02.100 --> 00:02:09.220 which is transforming the way we manufacture everything from life-saving medicines to biofuels and to silicon chips. 27 00:02:09.780 --> 00:02:12.860 And the courage of Associate Professor Damien Riggs, 28 00:02:13.080 --> 00:02:18.580 who fearlessly investigated the fields of race and whiteness and gender fluidity. 29 00:02:19.480 --> 00:02:24.680 Flinders has many, many more researchers who are equally persistent and fearless 30 00:02:24.680 --> 00:02:26.140 in their thirst for new knowledge 31 00:02:26.140 --> 00:02:28.260 and the desire to make a difference in the world. 32 00:02:29.140 --> 00:02:32.040 This morning, we're aiming to give you fresh insights 33 00:02:32.040 --> 00:02:36.120 into how artificial intelligence is being used in the health sector 34 00:02:36.120 --> 00:02:39.180 and what this means for healthy ageing moving forward. 35 00:02:40.120 --> 00:02:44.260 It is my great pleasure this morning to introduce Professor Alison Kitson, 36 00:02:44.400 --> 00:02:49.600 the Vice President Executive Dean for the College of Nursing and Health Science at Flinders University 37 00:02:50.140 --> 00:02:52.080 and your presenter for this morning's talk. 38 00:02:52.240 --> 00:02:53.200 Please welcome Alison. 39 00:02:57.700 --> 00:02:58.900 Thanks, Trish. 40 00:03:00.420 --> 00:03:05.920 I think it's the notion of bravery is certainly appropriate 41 00:03:05.920 --> 00:03:12.140 because as you heard, Trish is the Professor of Digital Health in Flinders 42 00:03:12.140 --> 00:03:16.120 and I actually thought it was an accident when I was asked to do this lecture 43 00:03:16.120 --> 00:03:20.780 on artificial intelligence and health because I thought, well, what do I know about that? 44 00:03:21.040 --> 00:03:25.480 But I certainly know a lot now after having thought about it 45 00:03:25.480 --> 00:03:30.440 and realizing that possibly the best thing about doing a lecture like this 46 00:03:30.440 --> 00:03:34.440 is that you've got someone like me who's not an expert 47 00:03:34.440 --> 00:03:39.240 talking to what I assume is a group of people who are not experts either. 48 00:03:39.500 --> 00:03:44.400 So I just want to check hands up in the audience who is an expert in artificial intelligence. 49 00:03:45.920 --> 00:03:46.480 Nobody. 50 00:03:46.660 --> 00:03:46.980 Nobody. 51 00:03:46.980 --> 00:03:47.940 Oh, there's one. 52 00:03:48.300 --> 00:03:48.860 Okay. 53 00:03:49.160 --> 00:03:53.780 So I'm going to ask you at the end of the presentation, did I do okay? 54 00:03:53.960 --> 00:03:54.700 Is that okay? 55 00:03:55.480 --> 00:03:55.980 Right, okay. 56 00:03:56.560 --> 00:04:04.340 So also, so bravery is about bravely saying yes to the invitation to do the talk. 57 00:04:05.480 --> 00:04:10.880 Brave new world, thinking about the future of what is going to be happening to us 58 00:04:10.880 --> 00:04:13.980 in the next 5, 10, 15, 20 years. 59 00:04:14.440 --> 00:04:22.400 And brave heart because emotions do play a part in the whole idea of artificial intelligence. 60 00:04:22.400 --> 00:04:27.240 So I would like also to acknowledge Michael Lawless, 61 00:04:27.520 --> 00:04:31.680 Jen Tiemann and Anthony Meador, who are all colleagues of mine 62 00:04:31.680 --> 00:04:38.360 and who have helped navigate me through understanding what artificial intelligence is 63 00:04:38.360 --> 00:04:41.140 and how it affects health. 64 00:04:41.480 --> 00:04:43.520 Anybody know what this is? 65 00:04:44.080 --> 00:04:44.880 Okay. 66 00:04:44.980 --> 00:04:47.080 This is called Colossus. 67 00:04:47.140 --> 00:04:52.040 Does anybody know which computer was Colossus? 68 00:04:52.400 --> 00:04:55.140 Anybody heard of the Enigma code? 69 00:04:55.860 --> 00:04:56.840 Yes, okay. 70 00:04:57.060 --> 00:05:05.060 So this is a picture of the actual computer that was responsible for helping the British 71 00:05:05.060 --> 00:05:10.660 to crack the code, the German code in the Second World War. 72 00:05:10.860 --> 00:05:16.260 And this is a very fetching picture of two young women trying to manage the machine. 73 00:05:16.560 --> 00:05:19.600 Now, that is only like 70 years ago. 74 00:05:19.720 --> 00:05:22.200 And some people in the audience, you know, 75 00:05:22.200 --> 00:05:27.520 would remember that possibly, but also would be born around that time. 76 00:05:27.680 --> 00:05:33.220 This is now the more typical understanding of how we use technology, 77 00:05:34.200 --> 00:05:38.380 which started off not so long ago, maybe two generations ago, 78 00:05:38.560 --> 00:05:46.980 that you had to have a whole house, a whole sort of big plant to hold it. 79 00:05:47.080 --> 00:05:51.500 And now probably there may only be 10% of us 80 00:05:51.500 --> 00:05:55.160 in this room who would not have some sort of gadget. 81 00:05:55.460 --> 00:06:00.660 And in fact, many of us would be proud owners of multiple gadgets 82 00:06:00.660 --> 00:06:02.380 that all talk to each other. 83 00:06:02.540 --> 00:06:06.120 So again, in possibly 20 years' time, 84 00:06:06.280 --> 00:06:08.880 we'll probably have them implanted inside us 85 00:06:09.500 --> 00:06:11.540 rather than having to carry them around. 86 00:06:11.760 --> 00:06:13.720 So it's a bit crazy. 87 00:06:15.820 --> 00:06:17.940 So I'm a nurse by background. 88 00:06:18.160 --> 00:06:21.480 So I want to sort of get you to think about 89 00:06:21.500 --> 00:06:25.760 in the future, which is going to be the more likely scenario. 90 00:06:27.080 --> 00:06:29.620 Does anybody know the nurse... 91 00:06:31.060 --> 00:06:33.260 this one here, Nurse Ratched? 92 00:06:33.760 --> 00:06:36.120 Okay, so I want to... 93 00:06:36.120 --> 00:06:40.100 This is a little sort of reflection 94 00:06:40.100 --> 00:06:43.620 on some of the ways that we think about artificial intelligence, 95 00:06:43.860 --> 00:06:49.680 because a lot of us think that there's something possibly scary or wrong about it. 96 00:06:49.700 --> 00:06:51.100 But I just want to remind you 97 00:06:51.500 --> 00:06:54.960 that human beings can be a bit scary and wrong as well. 98 00:06:55.200 --> 00:06:56.240 So I'll read this. 99 00:06:56.340 --> 00:06:58.940 Nurse Ratched is the head administrative nurse 100 00:06:58.940 --> 00:07:01.260 at the Salem Oregon State Hospital, 101 00:07:01.520 --> 00:07:04.280 a mental institution where she exercises 102 00:07:04.280 --> 00:07:07.380 near absolute power over the patient's access 103 00:07:07.380 --> 00:07:10.940 to medications, privileges, and basic necessities 104 00:07:10.940 --> 00:07:12.500 such as food and toiletries. 105 00:07:12.740 --> 00:07:15.980 She capriciously revokes these privileges 106 00:07:15.980 --> 00:07:18.040 whenever a patient displeases her. 107 00:07:18.460 --> 00:07:20.220 Has anybody met nurses like that? 108 00:07:21.500 --> 00:07:22.240 Oh, right, okay. 109 00:07:22.380 --> 00:07:24.760 Unfortunately, there's several nods in the audience. 110 00:07:25.100 --> 00:07:28.420 Her superiors turn blind eyes because she maintains order, 111 00:07:29.500 --> 00:07:31.020 keeping the patients from acting out 112 00:07:31.020 --> 00:07:34.500 either through antipsychotic or anticonvulsant drugs 113 00:07:34.500 --> 00:07:37.020 or her own brand of psychotherapy, 114 00:07:37.260 --> 00:07:40.920 which consists mostly of humiliating patients 115 00:07:40.920 --> 00:07:42.180 into doing her bidding. 116 00:07:42.760 --> 00:07:44.980 Okay, so these are the sorts of people 117 00:07:44.980 --> 00:07:47.400 we don't really like to attract into nursing, 118 00:07:47.600 --> 00:07:49.300 but they get in there, 119 00:07:49.420 --> 00:07:51.300 and sometimes they're rewarded 120 00:07:51.500 --> 00:07:54.340 perversely by the status quo. 121 00:07:54.440 --> 00:07:58.120 Now, I want you to keep that sort of idea in your head 122 00:07:58.120 --> 00:07:59.660 because we're going to come back to that. 123 00:07:59.920 --> 00:08:04.740 So what are the wicked problems that we face in health 124 00:08:04.740 --> 00:08:08.200 that we think that artificial intelligence 125 00:08:08.200 --> 00:08:10.420 can help us solve? 126 00:08:10.780 --> 00:08:15.760 So the first wicked problem is that in every country, 127 00:08:16.520 --> 00:08:19.000 in every health system around the world, 128 00:08:19.040 --> 00:08:20.900 the cost of health care is... 129 00:08:21.500 --> 00:08:24.760 just skyrocketing, exponentially growing. 130 00:08:27.140 --> 00:08:28.260 It's both... 131 00:08:28.260 --> 00:08:30.240 It's strange that we call it a wicked problem, 132 00:08:30.500 --> 00:08:32.620 but the fact that we can all live longer 133 00:08:33.280 --> 00:08:35.860 is seen as definitely a benefit, 134 00:08:36.080 --> 00:08:39.680 but because it's never happened in our society before, 135 00:08:40.600 --> 00:08:44.100 we're sort of at a loss to know how we do that effectively, 136 00:08:45.200 --> 00:08:49.080 enabling people to live long, productive, healthy lives. 137 00:08:50.320 --> 00:08:51.720 Added to the longevity, 138 00:08:52.140 --> 00:08:56.340 we have got issues of multiple comorbidities 139 00:08:56.340 --> 00:08:59.300 or multiple illnesses that people gather, 140 00:09:00.100 --> 00:09:02.640 a bit like sort of dust. 141 00:09:02.940 --> 00:09:06.080 You know, you sort of gather them as you go along. 142 00:09:07.080 --> 00:09:12.020 And that is determined by our lifestyle choices, 143 00:09:12.180 --> 00:09:14.920 and we just heard Carmen Lawrence talking about 144 00:09:14.920 --> 00:09:16.800 the importance of, you know, 145 00:09:16.800 --> 00:09:18.380 the things that we would think possibly 146 00:09:18.380 --> 00:09:20.520 are common sense, but not so common. 147 00:09:21.320 --> 00:09:23.040 Walking, eating well, 148 00:09:23.840 --> 00:09:27.380 sort of being out in the open air and enjoying ourselves. 149 00:09:28.040 --> 00:09:30.980 And then one that I want to add as well into this list 150 00:09:30.980 --> 00:09:34.980 is the potential loss of the ability to care for each other. 151 00:09:35.160 --> 00:09:37.300 So this notion of how do we care, 152 00:09:37.480 --> 00:09:39.620 how do we learn to care for each other, 153 00:09:39.720 --> 00:09:41.560 and what does caring mean 154 00:09:41.560 --> 00:09:46.920 in the context of artificial intelligence and health? 155 00:09:46.920 --> 00:09:51.660 So just to check that we all are on the same page 156 00:09:51.660 --> 00:09:54.580 in understanding what AI is, 157 00:09:55.520 --> 00:09:59.740 it's basically it's intelligence demonstrated by machines. 158 00:10:00.200 --> 00:10:02.880 So then you have to think, well, what is intelligence? 159 00:10:03.640 --> 00:10:05.920 Well, it's something that happens 160 00:10:05.920 --> 00:10:08.300 in that part of our body called the brain. 161 00:10:08.520 --> 00:10:10.620 So what is human intelligence? 162 00:10:10.980 --> 00:10:15.020 Well, certainly most would agree 163 00:10:15.020 --> 00:10:16.900 that intelligence is related 164 00:10:16.920 --> 00:10:20.260 to problem solving, analyzing data, 165 00:10:20.420 --> 00:10:21.700 and pattern recognition. 166 00:10:22.020 --> 00:10:24.920 So those are the sorts of aspects 167 00:10:24.920 --> 00:10:28.920 of our ability to interpret the world. 168 00:10:29.420 --> 00:10:32.440 Also, human intelligence is related 169 00:10:32.440 --> 00:10:35.860 to the ability to communicate through speech and language 170 00:10:35.860 --> 00:10:39.440 and other aspects such as creativity, 171 00:10:39.880 --> 00:10:43.120 emotional intelligence, and morality and values. 172 00:10:43.460 --> 00:10:46.900 There's a little more debate about those aspects 173 00:10:46.920 --> 00:10:48.680 and dimensions of our understanding. 174 00:10:49.080 --> 00:10:52.460 Now, you can see that all of the issues 175 00:10:52.460 --> 00:10:54.800 on the left hand, is it? 176 00:10:54.920 --> 00:10:56.240 No, your right hand side. 177 00:10:56.600 --> 00:11:01.000 These would be the things that we would commonly know 178 00:11:01.000 --> 00:11:04.280 that artificial intelligence has grasped. 179 00:11:04.920 --> 00:11:06.940 So problem solving, data analysis, 180 00:11:07.220 --> 00:11:09.400 pattern recognition, speech and language, 181 00:11:09.580 --> 00:11:12.100 whereas the aspects of creativity, 182 00:11:12.920 --> 00:11:16.160 emotional intelligence, the morality and values, 183 00:11:16.160 --> 00:11:18.800 these are things that the science fiction, 184 00:11:19.160 --> 00:11:21.220 you know, the people who do Terminator 2, 185 00:11:21.380 --> 00:11:24.320 where you have the robots becoming malevolent, 186 00:11:24.360 --> 00:11:26.280 that's the stuff in the future 187 00:11:26.280 --> 00:11:27.760 that we're going to have to think about. 188 00:11:27.880 --> 00:11:31.920 But at the minute, we're getting our intelligence 189 00:11:31.920 --> 00:11:35.840 to do the sort of data analytics. 190 00:11:36.240 --> 00:11:38.900 So what are the five promising applications 191 00:11:38.900 --> 00:11:41.960 of artificial intelligence in health? 192 00:11:42.240 --> 00:11:44.940 Well, certainly there's data science 193 00:11:44.940 --> 00:11:45.840 and machine learning. 194 00:11:46.020 --> 00:11:47.740 So coming from Colossus, 195 00:11:47.880 --> 00:11:50.520 we have now got the capability 196 00:11:50.520 --> 00:11:53.740 to harvest huge amounts of data. 197 00:11:54.240 --> 00:11:58.000 Secondly, we've also got artificial intelligence 198 00:11:58.000 --> 00:12:02.580 helping us to embrace clinical decision support systems, 199 00:12:02.840 --> 00:12:05.180 better management of health and wellness, 200 00:12:06.580 --> 00:12:08.840 advanced assistive technologies 201 00:12:08.840 --> 00:12:11.200 and remote health service delivery. 202 00:12:11.500 --> 00:12:14.620 So these are the five major dimensions 203 00:12:14.940 --> 00:12:17.800 and I'm going to go through each of them in turn. 204 00:12:18.660 --> 00:12:20.300 But before I do that, 205 00:12:20.420 --> 00:12:23.220 if you look at all of the... 206 00:12:23.220 --> 00:12:25.020 put all of those things together, 207 00:12:26.260 --> 00:12:29.120 a review by Accenture, 208 00:12:29.180 --> 00:12:32.580 which used to be called Arthur Anderson Consulting, 209 00:12:33.840 --> 00:12:38.620 again, a very prestigious consultancy, 210 00:12:38.720 --> 00:12:42.360 have identified how some of this 211 00:12:42.360 --> 00:12:43.940 artificial intelligence applications 212 00:12:43.940 --> 00:12:45.960 could change healthcare. 213 00:12:46.300 --> 00:12:49.680 And you can see that they've looked at the application. 214 00:12:50.060 --> 00:12:53.140 They've addressed the first wicked problem, 215 00:12:53.380 --> 00:12:54.100 which is cost. 216 00:12:54.360 --> 00:12:56.800 And then they have identified the things 217 00:12:56.800 --> 00:13:00.000 that might change the way that we behave. 218 00:13:00.280 --> 00:13:02.940 Isn't it interesting that the very first one 219 00:13:02.940 --> 00:13:06.960 that would save $40 billion in the US 220 00:13:06.960 --> 00:13:09.480 is robot-assisted surgery? 221 00:13:09.880 --> 00:13:12.080 Now, hands up who, 222 00:13:12.220 --> 00:13:13.920 if you were going for surgery, 223 00:13:13.940 --> 00:13:20.940 and the doctor who asked you to sign your consent form said, 224 00:13:21.860 --> 00:13:24.180 oh, by the way, it's going to be a robot 225 00:13:24.180 --> 00:13:28.200 who's going to do the surgery 226 00:13:28.200 --> 00:13:31.160 because robots do it more reliably. 227 00:13:31.500 --> 00:13:32.920 Would you believe them? 228 00:13:33.720 --> 00:13:35.280 Yes, you would? Okay. 229 00:13:35.400 --> 00:13:36.720 Yeah, well done. 230 00:13:36.900 --> 00:13:40.000 So we're a very sort of advanced group. 231 00:13:41.280 --> 00:13:43.920 So again, you can see that there's lots of things 232 00:13:43.920 --> 00:13:44.040 that we're going to address. 233 00:13:44.040 --> 00:13:47.360 There's lots of big sort of social, 234 00:13:47.600 --> 00:13:52.520 cultural trust issues in this whole way 235 00:13:52.520 --> 00:13:55.820 that we are going to embrace artificial intelligence. 236 00:13:56.600 --> 00:13:59.560 The first one is around big data sets. 237 00:14:00.000 --> 00:14:05.040 Now, again, it's, again, going back to the example 238 00:14:05.040 --> 00:14:07.400 of the Enigma code. 239 00:14:07.660 --> 00:14:10.860 Has anybody seen the imitation game? 240 00:14:10.860 --> 00:14:12.480 That's the movie. 241 00:14:13.000 --> 00:14:14.920 And I didn't realize, 242 00:14:15.240 --> 00:14:17.820 and I don't actually know whether it's true or not, 243 00:14:17.900 --> 00:14:20.120 but it said it in the movie, so it must be, 244 00:14:22.360 --> 00:14:25.760 that how Alan Turing first, 245 00:14:25.940 --> 00:14:27.920 his first big break, 246 00:14:28.140 --> 00:14:30.940 and that's not a pun, into breaking the code, 247 00:14:31.080 --> 00:14:34.580 was he overheard two people talking, 248 00:14:34.720 --> 00:14:37.780 and one of the telephonists 249 00:14:37.780 --> 00:14:39.680 who talked to a German, 250 00:14:39.680 --> 00:14:42.640 a German telephonist, 251 00:14:42.840 --> 00:14:45.980 they always said Heil Hitler 252 00:14:45.980 --> 00:14:49.160 was the very first two words that they said. 253 00:14:49.320 --> 00:14:52.800 So he then realized that every time he got a code, 254 00:14:52.960 --> 00:14:55.720 Heil Hitler was going to be the first two words in it. 255 00:14:55.880 --> 00:14:58.760 So he worked out from those letters 256 00:14:58.760 --> 00:15:01.840 how if each time that pattern came, 257 00:15:02.080 --> 00:15:04.900 that these would be the H's or the G's. 258 00:15:05.040 --> 00:15:05.800 And then I was thinking, 259 00:15:05.940 --> 00:15:07.880 that's a bit like doing crossword, 260 00:15:08.000 --> 00:15:09.080 or, you know, 261 00:15:09.080 --> 00:15:12.220 the sort of numbers games that people do on TV, 262 00:15:12.400 --> 00:15:15.160 and you have got ten consonants and eight vowels, 263 00:15:15.240 --> 00:15:16.180 and you have to make words. 264 00:15:16.360 --> 00:15:22.560 So there's something intrinsically hardwired into our heads 265 00:15:22.560 --> 00:15:25.560 where we actually like solving those sorts of problems. 266 00:15:25.860 --> 00:15:28.140 So think about that, 267 00:15:28.240 --> 00:15:31.980 and then think about the way that organizations 268 00:15:32.980 --> 00:15:36.560 can collect data about diseases. 269 00:15:36.760 --> 00:15:39.060 So most countries have one, 270 00:15:39.080 --> 00:15:39.580 and they have a lot of registers. 271 00:15:39.780 --> 00:15:41.720 You've got a cancer register, 272 00:15:42.020 --> 00:15:43.540 you've got an orthopedic register, 273 00:15:43.880 --> 00:15:46.880 you've got all sorts of registers 274 00:15:46.880 --> 00:15:49.400 that describe what has happened to people 275 00:15:49.400 --> 00:15:50.780 who've got certain illnesses. 276 00:15:51.120 --> 00:15:53.980 In fact, I heard a talk recently 277 00:15:53.980 --> 00:15:58.380 about a computing firm 278 00:15:58.380 --> 00:16:03.080 that has just recruited 20 million cancer patients 279 00:16:03.700 --> 00:16:07.240 onto their cancer registry across the United States. 280 00:16:07.420 --> 00:16:08.880 And that's because, 281 00:16:09.080 --> 00:16:12.320 they know that if they have that volume of data 282 00:16:12.320 --> 00:16:15.700 about people's individual cancer journeys, 283 00:16:15.920 --> 00:16:19.560 they will be able to do the deep analytical work on it 284 00:16:19.560 --> 00:16:20.900 to help look at the patterns, 285 00:16:21.000 --> 00:16:23.900 to help think about how they can understand 286 00:16:23.900 --> 00:16:27.020 both how to give better support, 287 00:16:27.440 --> 00:16:30.680 how to have more targeted care, 288 00:16:30.980 --> 00:16:33.900 and how to solve some of the problems. 289 00:16:34.260 --> 00:16:35.980 So it's, 290 00:16:35.980 --> 00:16:37.700 the reality is that 291 00:16:37.700 --> 00:16:40.880 the country borders no longer exist 292 00:16:40.880 --> 00:16:43.800 in these sorts of large data sets. 293 00:16:44.780 --> 00:16:47.680 Equally, given that we now, 294 00:16:47.900 --> 00:16:49.300 like in Iceland, 295 00:16:49.640 --> 00:16:54.140 every one of the population has had their DNA analyzed, 296 00:16:54.480 --> 00:16:56.820 and that is sitting on a large data bank. 297 00:16:57.140 --> 00:17:00.900 Think of that across the world. 298 00:17:01.100 --> 00:17:05.000 So, and that is what personalized medicine's all about. 299 00:17:05.260 --> 00:17:07.680 The biological insights and the time 300 00:17:07.700 --> 00:17:09.840 and the targeted therapies that will be possible 301 00:17:09.840 --> 00:17:12.440 because we have these huge data sets. 302 00:17:13.500 --> 00:17:17.440 Now, you know, that's amazing, isn't it? 303 00:17:17.580 --> 00:17:21.800 But think of all of the positive things that could happen, 304 00:17:21.860 --> 00:17:23.920 but think of all of the unintended 305 00:17:23.920 --> 00:17:26.500 or scary things that could happen. 306 00:17:27.020 --> 00:17:29.600 Because we've got these large data sets, 307 00:17:29.840 --> 00:17:34.320 the second area around is clinical decision support systems. 308 00:17:34.640 --> 00:17:36.620 Now, medicine, nursing, 309 00:17:37.660 --> 00:17:39.800 physiotherapy, anything in health 310 00:17:39.800 --> 00:17:43.300 is about analyzing data, 311 00:17:43.580 --> 00:17:45.100 looking for patterns, 312 00:17:45.220 --> 00:17:47.240 and being able to make a good judgment. 313 00:17:47.560 --> 00:17:49.260 If you are a clinician, 314 00:17:50.120 --> 00:17:53.620 you may have 3,000 different cases 315 00:17:54.240 --> 00:17:56.820 of X, Y, or Z that you're looking at, 316 00:17:56.920 --> 00:17:58.480 and you will be able to make a decision. 317 00:17:59.100 --> 00:18:01.520 If you are a physician, 318 00:18:01.740 --> 00:18:04.400 a nurse, a physiotherapist, 319 00:18:04.400 --> 00:18:06.160 with an iPad in front of you, 320 00:18:06.620 --> 00:18:09.860 and you have access to these large data sets, 321 00:18:10.140 --> 00:18:13.920 you no longer have 3,000 possible cases 322 00:18:13.920 --> 00:18:16.500 that your mind can sift through. 323 00:18:16.660 --> 00:18:19.900 You can have 3 million or 300 million 324 00:18:19.900 --> 00:18:23.060 because the ability of those machines, 325 00:18:23.380 --> 00:18:25.800 potentially and in reality, 326 00:18:26.020 --> 00:18:28.320 to do that is quite significant. 327 00:18:28.720 --> 00:18:32.380 So this is the way that health professionals are taught, 328 00:18:34.040 --> 00:18:36.100 analytical gathering data, 329 00:18:36.100 --> 00:18:39.300 we also have got now 330 00:18:39.300 --> 00:18:42.020 diagnostic decision support tools, 331 00:18:42.180 --> 00:18:45.220 so anyone who is a health professional 332 00:18:45.220 --> 00:18:48.220 in the audience will have the joy 333 00:18:48.220 --> 00:18:51.980 of discovering EPAS, 334 00:18:52.260 --> 00:18:55.680 that electronic patient record 335 00:18:55.680 --> 00:18:59.060 that South Australia has embraced. 336 00:18:59.420 --> 00:19:03.040 Now, that is the sort of first step 337 00:19:03.040 --> 00:19:05.100 to trying to monitor 338 00:19:05.280 --> 00:19:07.300 and modernise and improve 339 00:19:07.300 --> 00:19:09.660 and connect lots of information. 340 00:19:10.660 --> 00:19:13.420 Other systems are much more advanced, 341 00:19:13.540 --> 00:19:15.920 and again, the health record, 342 00:19:16.220 --> 00:19:19.080 people are probably familiar with that. 343 00:19:19.440 --> 00:19:21.440 That again is another step 344 00:19:21.440 --> 00:19:23.720 to starting to enable us 345 00:19:23.720 --> 00:19:26.400 to be able to share data more effectively. 346 00:19:27.160 --> 00:19:30.040 Equally, in the area of diagnostics, 347 00:19:31.100 --> 00:19:33.180 you will know that, again, 348 00:19:33.300 --> 00:19:34.620 research shows that 349 00:19:35.280 --> 00:19:39.900 certain computer artificial intelligence systems 350 00:19:39.900 --> 00:19:42.360 can more accurately identify 351 00:19:43.280 --> 00:19:48.640 and interpret sort of pathological slides or X-rays. 352 00:19:48.900 --> 00:19:51.840 So again, we will be seeing that things 353 00:19:51.840 --> 00:19:56.080 that require a lot of analytical pattern recognition, 354 00:19:56.340 --> 00:19:58.320 this is the sort of capability 355 00:19:58.320 --> 00:20:00.840 that our artificial intelligence has. 356 00:20:01.380 --> 00:20:05.020 Now, again, there is probably 357 00:20:05.280 --> 00:20:08.180 most of the people in the audience 358 00:20:08.180 --> 00:20:12.600 are familiar with the healthcare app, 359 00:20:12.800 --> 00:20:14.860 just like you've got your taxi app, 360 00:20:14.980 --> 00:20:16.200 you've got your healthcare app. 361 00:20:16.340 --> 00:20:19.660 My husband and I have just been competing 362 00:20:19.660 --> 00:20:25.580 with each other as to who can sort of do more steps, 363 00:20:25.820 --> 00:20:28.060 until I realised that he said, 364 00:20:28.280 --> 00:20:29.440 well, if you do this, 365 00:20:31.420 --> 00:20:33.980 you can increase the number of your steps. 366 00:20:33.980 --> 00:20:36.760 I wasn't terribly impressed with that. 367 00:20:38.940 --> 00:20:42.760 Also, not only can you do it for health benefit 368 00:20:42.760 --> 00:20:44.600 or for exercise, 369 00:20:44.820 --> 00:20:47.740 but equally, there's work being done in the US 370 00:20:47.740 --> 00:20:49.300 where you could actually, 371 00:20:49.480 --> 00:20:50.840 your telephone could tell you 372 00:20:50.840 --> 00:20:52.600 whether you're having a heart attack or not. 373 00:20:52.880 --> 00:20:56.120 That could be life-saving. 374 00:20:57.100 --> 00:21:00.740 And I was in the UK just last week, 375 00:21:00.780 --> 00:21:02.660 and I picked up a Vogue magazine, 376 00:21:02.660 --> 00:21:06.420 and so this is what they said in Vogue magazine. 377 00:21:06.680 --> 00:21:08.100 The apps to know, 378 00:21:08.800 --> 00:21:09.460 clue, 379 00:21:09.740 --> 00:21:11.640 track your menstrual cycle 380 00:21:11.640 --> 00:21:13.480 and its associated symptoms, 381 00:21:13.540 --> 00:21:15.600 such as energy and appetite levels, 382 00:21:15.800 --> 00:21:18.020 as well as skin and mood changes, 383 00:21:18.060 --> 00:21:18.680 good. 384 00:21:19.020 --> 00:21:19.680 Glow, 385 00:21:19.680 --> 00:21:22.220 gain insight into your reproductive health 386 00:21:22.220 --> 00:21:24.340 with this popular ovulation tracker 387 00:21:24.340 --> 00:21:26.720 that highlights your most fertile days. 388 00:21:27.060 --> 00:21:28.740 So these are things that, 389 00:21:28.800 --> 00:21:30.700 and apparently there's something like 390 00:21:30.700 --> 00:21:32.300 17 million users, 391 00:21:32.300 --> 00:21:33.240 of this app. 392 00:21:33.440 --> 00:21:36.460 Now I want an app called Menno app. 393 00:21:38.880 --> 00:21:39.480 Yeah. 394 00:21:40.080 --> 00:21:42.440 Sisters, let's go for the Menno app. 395 00:21:42.980 --> 00:21:43.580 Yeah. 396 00:21:43.720 --> 00:21:46.500 And then I think there should be one called 397 00:21:46.500 --> 00:21:47.980 the Bullshitter app as well. 398 00:21:48.960 --> 00:21:51.780 Because that would be very, very helpful. 399 00:21:51.920 --> 00:21:53.620 And another app called 400 00:21:53.620 --> 00:21:55.300 How to Avoid Boring People at Parties. 401 00:21:57.680 --> 00:22:00.360